Federated-Learning-on-Graph-and-Tabular-Data

Table of Contents

Papers
- FL on Graph Data and Graph Neural Networks
- FL on Tabular Data
- FL in top-tier journal
- FL in top-tier conference and journal by category
  - AI ML DM Secure CV NLP IR DB Network System Others
Framework
Datasets
Surveys
Tutorials and Courses
Key Conferences/Workshops/Journals
- Workshops Special Issues Special Tracks
Update log
How to contact us
Acknowledgments
Citation

papers

categories

Artificial Intelligence (IJCAI, AAAI, AISTATS, AI)
Machine Learning (NeurIPS, ICML, ICLR, COLT, UAI, JMLR, TPAMI)
Data Mining (KDD, WSDM)
Secure (S&P, CCS, USENIX Security, NDSS)
Computer Vision (ICCV, CVPR, ECCV, MM, IJCV)
Natural Language Processing (ACL, EMNLP, NAACL, COLING)
Information Retrieval (SIGIR)
Database (SIGMOD, ICDE, VLDB)
Network (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW)
System (OSDI, SOSP, ISCA, MLSys, TPDS, DAC, TOCS, TOS, TCAD, TC)
Others (ICSE)

keywords

Statistics: 🔥 code is available & stars >= 100 | ⭐ citation >= 50 | 🎓 Top-tier venue

kg.: Knowledge Graph | data.: dataset | surv.: survey

fl on graph data and graph neural networks

This section partially refers to DBLP search engine and repositories Awesome-Federated-Learning-on-Graph-and-GNN-papers and Awesome-Federated-Machine-Learning.

Title	Affiliation	Venue	Year	TL;DR	Materials
Personalized Subgraph Federated Learning	KAIST	ICML 🎓	2023	FED-PUB¹	[PDF]
Semi-decentralized Federated Ego Graph Learning for Recommendation	SUST	WWW🎓	2023		[PUB] [PDF]
Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks	ECUST	IEEE Trans. Inf. Forensics Secur. 🎓	2023		[PUB]
Federated Learning Over Coupled Graphs	XJTU	IEEE Trans. Parallel Distributed Syst. 🎓	2023		[PUB] [PDF]
HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning	Nankai University	IEEE Trans. Vis. Comput. Graph. 🎓	2023	HetVis²	[PUB] [PDF]
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing	UTS	AAAI 🎓	2023	FedStar³	[PDF] [CODE]
FedGS: Federated Graph-based Sampling with Arbitrary Client Availability	XMU	AAAI 🎓	2023	FedGS⁴	[PDF] [CODE]
Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning	ZUEL	IEEE Trans. Intell. Transp. Syst.	2023		[PUB]
Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection.	HVL	IEEE J. Biomed. Health Informatics	2023		[PUB]
Federated Learning-Based Cross-Enterprise Recommendation With Graph Neural		IEEE Trans. Ind. Informatics	2023	FL-GMT⁵	[PUB]
FedGR: Federated Graph Neural Network for Recommendation System	CUPT	Axioms	2023		[PUB]
GDFed: Dynamic Federated Learning for Heterogenous Device Using Graph Neural Network	KHU	ICOIN	2023		[PUB] [CODE]
Coordinated Scheduling and Decentralized Federated Learning Using Conflict Clustering Graphs in Fog-Assisted IoD Networks	UBC	IEEE Trans. Veh. Technol.	2023		[PUB]
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy	SJTU	KDD 🎓	2022	FedWalk⁶	[PUB] [PDF]
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning 🔥	Alibaba	KDD (Best Paper Award) 🎓	2022	FederatedScope-GNN⁷	[PDF] [CODE] [PUB]
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning	SJTU	ICML 🎓	2022	GAMF⁸	[PUB] [CODE]
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting `kg.`	ZJU	IJCAI 🎓	2022	MaKEr⁹	[PUB] [PDF] [CODE]
Personalized Federated Learning With a Graph	UTS	IJCAI 🎓	2022	SFL¹⁰	[PUB] [PDF] [CODE]
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification	ZJU	IJCAI 🎓	2022	VFGNN¹¹	[PUB] [PDF]
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data	USC	AAAI🎓	2022	SpreadGNN¹²	[PUB] [PDF] [CODE] [解读]
FedGraph: Federated Graph Learning with Intelligent Sampling	UoA	TPDS 🎓	2022	FedGraph¹³	[PUB] [CODE] [解读]
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications `surv.`	University of Virginia	SIGKDD Explor.	2022	FGML¹⁴	[PUB] [PDF]
Semantic Vectorization: Text- and Graph-Based Models.	IBM Research	Federated Learning	2022		[PUB]
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs	IIT	ICDM	2022	GraphFL¹⁵	[PUB] [PDF] [解读]
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks	TU Delft	ACSAC	2022		[PUB] [PDF]
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction	UESTC	TMI	2022	FedNI¹⁶	[PUB] [PDF]
SemiGraphFL: Semi-supervised Graph Federated Learning for Graph Classification.	PKU	PPSN	2022	SemiGraphFL¹⁷	[PUB]
Federated Spatio-Temporal Traffic Flow Prediction Based on Graph Convolutional Network	TJU	WCSP	2022		[PUB]
A federated graph neural network framework for privacy-preserving personalization	THU	Nature Communications	2022	FedPerGNN¹⁸	[PUB] [CODE] [解读]
Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning	BIT	INFOCOM Workshops	2022	GraphSniffer¹⁹	[PUB]
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation `kg.`	Lehigh University	EMNLP	2022	FedR²⁰	[PUB] [PDF] [CODE]
Power Allocation for Wireless Federated Learning using Graph Neural Networks	Rice University	ICASSP	2022	wirelessfl-pdgnet²¹	[PUB] [PDF] [CODE]
Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization	UC	ICASSP	2022	multitask-fusion²²	[PUB] [PDF] [CODE]
Graph-regularized federated learning with shareable side information	NWPU	Knowl. Based Syst.	2022		[PUB]
Federated knowledge graph completion via embedding-contrastive learning `kg.`	ZJU	Knowl. Based Syst.	2022	FedEC²³	[PUB]
Federated Graph Learning with Periodic Neighbour Sampling	HKU	IWQoS	2022	PNS-FGL²⁴	[PUB]
FedGSL: Federated Graph Structure Learning for Local Subgraph Augmentation.		Big Data	2022		[PUB]
Domain-Aware Federated Social Bot Detection with Multi-Relational Graph Neural Networks.	UCAS; CAS	IJCNN	2022	DA-MRG²⁵	[PUB]
A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy	Ping An Technology	KSEM	2022	DP-FedRec²⁶	[PUB] [PDF]
Clustered Graph Federated Personalized Learning.	NTNU	IEEECONF	2022		[PUB]
FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks	CMU	CIKM Workshop (Oral)	2022	FedGCN²⁷	[PDF] [CODE]
Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets.		MICCAI Workshop	2022		[PDF] [CODE]
Peer-to-Peer Variational Federated Learning Over Arbitrary Graphs	UCSD	Int. J. Bio Inspired Comput.	2022		[PUB]
Federated Multi-task Graph Learning	ZJU	ACM Trans. Intell. Syst. Technol.	2022		[PUB]
Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning	SUSTech	WCNC	2022	CTFL²⁸	[PUB]
Federated meta-learning for spatial-temporal prediction	NEU	Neural Comput. Appl.	2022	FML-ST²⁹	[PUB] [CODE]
BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning	NTU	IEEE Transactions on Big Data	2022	BiG-Fed³⁰	[PUB] [PDF]
Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning	Missouri S&T	INFCOM Workshops	2022	FL-ST³¹	[PUB]
Federated learning of molecular properties with graph neural networks in a heterogeneous setting	University of Rochester	Patterns	2022	FLIT+³²	[PUB] [PDF] [CODE]
Graph Federated Learning for CIoT Devices in Smart Home Applications	University of Toronto	IEEE Internet Things J.	2022		[PUB] [PDF] [CODE]
Multi-Level Federated Graph Learning and Self-Attention Based Personalized Wi-Fi Indoor Fingerprint Localization	SYSU	IEEE Commun. Lett.	2022	ML-FGL³³	[PUB]
Graph-Assisted Communication-Efficient Ensemble Federated Learning	UC	EUSIPCO	2022		[PUB] [PDF]
Decentralized Graph Federated Multitask Learning for Streaming Data	NTNU	CISS	2022	PSO-GFML³⁴	[PUB]
Neural graph collaborative filtering for privacy preservation based on federated transfer learning		Electron. Libr.	2022	FTL-NGCF³⁵	[PUB]
Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking in Healthcare Applications	Oxford	JBHI	2022	DNG-FR³⁶	[PUB]
FedGCN: Federated Learning-Based Graph Convolutional Networks for Non-Euclidean Spatial Data	NUIST	Mathematics	2022	FedGCN-NES³⁷	[PUB]
Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance	ND	ACM Trans. Intell. Syst. Technol.	2022	Feddy³⁸	[PUB] [PDF] [解读]
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation.	Purdue	INFOCOM 🎓	2021	D2D-FedL³⁹	[PUB] [PDF]
Federated Graph Classification over Non-IID Graphs	Emory	NeurIPS 🎓	2021	GCFL⁴⁰	[PUB] [PDF] [CODE] [解读]
Subgraph Federated Learning with Missing Neighbor Generation	Emory; UBC; Lehigh University	NeurIPS 🎓	2021	FedSage⁴¹	[PUB] [PDF]
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling	USC	KDD 🎓	2021	CNFGNN⁴²	[PUB] [PDF] [CODE] [解读]
Differentially Private Federated Knowledge Graphs Embedding `kg.`	BUAA	CIKM	2021	FKGE⁴³	[PUB] [PDF] [CODE] [解读]
Decentralized Federated Graph Neural Networks	Blue Elephant Tech	IJCAI Workshop	2021	D-FedGNN⁴⁴	[PDF]
FedSGC: Federated Simple Graph Convolution for Node Classification	HKUST	IJCAI Workshop	2021	FedSGC⁴⁵	[PDF]
FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper	UNM	ICCAD	2021	FL-DISCO⁴⁶	[PUB]
FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting	UTS	IEEE Trans. Ind. Informatics	2021	FASTGNN⁴⁷	[PUB]
DAG-FL: Direct Acyclic Graph-based Blockchain Empowers On-Device Federated Learning	BUPT; UESTC	ICC	2021	DAG-FL⁴⁸	[PUB] [PDF]
FedE: Embedding Knowledge Graphs in Federated Setting `kg.`	ZJU	IJCKG	2021	FedE⁴⁹	[PUB] [PDF] [CODE]
Federated Knowledge Graph Embeddings with Heterogeneous Data `kg.`	TJU	CCKS	2021	FKE⁵⁰	[PUB]
A Graph Federated Architecture with Privacy Preserving Learning	EPFL	SPAWC	2021	GFL⁵¹	[PUB] [PDF] [解读]
Federated Social Recommendation with Graph Neural Network	UIC	ACM TIST	2021	FeSoG⁵²	[PUB] [PDF] [CODE]
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks 🔥 `surv.`	USC	ICLR Workshop / MLSys Workshop	2021	FedGraphNN⁵³	[PDF] [CODE] [解读]
A Federated Multigraph Integration Approach for Connectional Brain Template Learning	Istanbul Technical University	MICCAI Workshop	2021	Fed-CBT⁵⁴	[PUB] [CODE]
Cluster-driven Graph Federated Learning over Multiple Domains	Politecnico di Torino	CVPR Workshop	2021	FedCG-MD⁵⁵	[PDF] [解读]
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation	THU	ICML workshop	2021	FedGNN⁵⁶	[PDF] [解读]
Decentralized federated learning of deep neural networks on non-iid data	RISE; Chalmers University of Technology	ICML workshop	2021	DFL-PENS⁵⁷	[PDF] [CODE]
Glint: Decentralized Federated Graph Learning with Traffic Throttling and Flow Scheduling	The University of Aizu	IWQoS	2021	Glint⁵⁸	[PUB]
Federated Graph Neural Network for Cross-graph Node Classification	BUPT	CCIS	2021	FGNN⁵⁹	[PUB]
GraFeHTy: Graph Neural Network using Federated Learning for Human Activity Recognition	Lead Data Scientist Ericsson Digital Services	ICMLA	2021	GraFeHTy⁶⁰	[PUB]
Distributed Training of Graph Convolutional Networks	Sapienza University of Rome	TSIPN	2021	D-GCN⁶¹	[PUB] [PDF] [解读]
Decentralized federated learning for electronic health records	UMN	NeurIPS Workshop / CISS	2020	FL-DSGD⁶²	[PUB] [PDF] [解读]
ASFGNN: Automated Separated-Federated Graph Neural Network	Ant Group	PPNA	2020	ASFGNN⁶³	[PUB] [PDF] [解读]
Decentralized federated learning via sgd over wireless d2d networks	SZU	SPAWC	2020	DSGD⁶⁴	[PUB] [PDF]
SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure	SDU	BigData	2019	SGNN⁶⁵	[PUB] [PDF]
Towards Federated Graph Learning for Collaborative Financial Crimes Detection	IBM	NeurIPS Workshop	2019	FGL-DFC⁶⁶	[PDF]
Federated learning of predictive models from federated Electronic Health Records ⭐	BU	Int. J. Medical Informatics	2018	cPDS⁶⁷	[PUB]
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data		preprint	2023		[PDF]
Vertical Federated Graph Neural Network for Recommender System		preprint	2023		[PDF] [CODE]
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices		preprint	2023		[PDF]
Securing IoT Communication using Physical Sensor Data - Graph Layer Security with Federated Multi-Agent Deep Reinforcement Learning.		preprint	2023		[PDF]
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning.		preprint	2023		[PDF]
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning		preprint	2023		[PDF]
Graph Federated Learning with Hidden Representation Sharing	UCLA	preprint	2022	GFL-APPNP⁶⁸	[PDF]
FedRule: Federated Rule Recommendation System with Graph Neural Networks	CMU	preprint	2022	FedRule⁶⁹	[PDF]
M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction	Xidian University	preprint	2022	M3FGM⁷⁰	[PDF]
Federated Graph-based Networks with Shared Embedding	BUCEA	preprint	2022		[PDF]
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph	Lancaster University	preprint	2022		[PDF]
Heterogeneous Federated Learning on a Graph.		preprint	2022		[PDF]
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs	SYSU	preprint	2022	FedEgo⁷¹	[PDF] [CODE]
Federated Graph Contrastive Learning	UTS	preprint	2022	FGCL⁷²	[PDF]
FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR		preprint	2022	FD-GATDR⁷³	[PDF]
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning		preprint	2022		[PDF]
Federated Graph Attention Network for Rumor Detection		preprint	2022		[PDF] [CODE]
FedRel: An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning		preprint	2022		[PDF]
Privatized Graph Federated Learning		preprint	2022		[PDF]
Federated Graph Neural Networks: Overview, Techniques and Challenges `surv.`		preprint	2022		[PDF]
Decentralized event-triggered federated learning with heterogeneous communication thresholds.		preprint	2022	EF-HC⁷⁴	[PDF]
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks		preprint	2022		[PDF]
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks		preprint	2021		[PDF] [CODE]
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning		preprint	2021		[PDF] [CODE]
PPSGCN: A Privacy-Preserving Subgraph Sampling Based Distributed GCN Training Method		preprint	2021	PPSGCN⁷⁵	[PDF]
Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries `kg.`		preprint	2021		[PDF]
Federated Myopic Community Detection with One-shot Communication		preprint	2021		[PDF]
Federated Graph Learning -- A Position Paper `surv.`		preprint	2021		[PDF]
A Vertical Federated Learning Framework for Graph Convolutional Network		preprint	2021	FedVGCN⁷⁶	[PDF]
FedGL: Federated Graph Learning Framework with Global Self-Supervision		preprint	2021	FedGL⁷⁷	[PDF]
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search		preprint	2021	FL-AGCNS⁷⁸	[PDF]
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach		preprint	2021		[PDF]
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization		preprint	2021	dFedU⁷⁹	[PDF] [CODE]
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty `kg.`		preprint	2020	FedAlign-KG⁸⁰	[PDF]
GraphFederator: Federated Visual Analysis for Multi-party Graphs		preprint	2020		[PDF]
Privacy-Preserving Graph Neural Network for Node Classification		preprint	2020		[PDF]
Peer-to-peer federated learning on graphs	UC	preprint	2019	P2P-FLG⁸¹	[PDF] [解读]

Private Graph Neural Networks (todo)

[Arxiv 2021] Privacy-Preserving Graph Convolutional Networks for Text Classification. [PDF]
[Arxiv 2021] GraphMI: Extracting Private Graph Data from Graph Neural Networks. [PDF]
[Arxiv 2021] Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning. [PDF]
[Arxiv 2020] Locally Private Graph Neural Networks. [PDF]

fl on tabular data

This section refers to DBLP search engine.

Title	Affiliation	Venue	Year	TL;DR	Materials
SGBoost: An Efficient and Privacy-Preserving Vertical Federated Tree Boosting Framework	Xidian University	IEEE Trans. Inf. Forensics Secur. 🎓	2023	SGBoost⁸²	[PUB] [CODE]
Incentive-boosted Federated Crowdsourcing	SDU	AAAI 🎓	2023	iFedCrowd⁸³	[PDF]
Explaining predictions and attacks in federated learning via random forests	Universitat Rovira i Virgili	Appl. Intell.	2023		[PUB] [CODE]
Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses		IEEE Trans. Ind. Informatics	2023		[PUB]
HT-Fed-GAN: Federated Generative Model for Decentralized Tabular Data Synthesis	HIT	Entropy	2023		[PUB]
Blockchain-Based Swarm Learning for the Mitigation of Gradient Leakage in Federated Learning	University of Udine	IEEE Access	2023		[PUB]
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization	ZJU	Proc. VLDB Endow. 🎓	2022	OpBoost⁸⁴	[PUB] [PDF] [CODE]
RevFRF: Enabling Cross-Domain Random Forest Training With Revocable Federated Learning	XIDIAN UNIVERSITY	IEEE Trans. Dependable Secur. Comput. 🎓	2022	RevFRF⁸⁵	[PUB] [PDF]
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources	University of Pittsburgh	ICML 🎓	2022		[PUB] [PDF] [CODE]
Federated Boosted Decision Trees with Differential Privacy	University of Warwick	CCS 🎓	2022		[PUB] [PDF] [CODE]
Federated Functional Gradient Boosting	University of Pennsylvania	AISTATS 🎓	2022	FFGB⁸⁶	[PUB] [PDF] [CODE]
Tree-Based Models for Federated Learning Systems.	IBM Research	Federated Learning	2022		[PUB]
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case		ICCP	2022		[PUB]
Federated Learning for Tabular Data: Exploring Potential Risk to Privacy	Newcastle University	ISSRE	2022		[PDF]
Federated Random Forests can improve local performance of predictive models for various healthcare applications	University of Marburg	Bioinform.	2022	FRF⁸⁷	[PUB] [CODE]
Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent.	unito	IJCNN	2022	federation-boosting⁸⁸	[PUB] [CODE]
Federated Forest	JD	TBD	2022	FF⁸⁹	[PUB] [PDF]
Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.		AMIA	2022		[PUB]
Fed-GBM: a cost-effective federated gradient boosting tree for non-intrusive load monitoring	The University of Sydney	e-Energy	2022	Fed-GBM⁹⁰	[PUB]
Verifiable Privacy-Preserving Scheme Based on Vertical Federated Random Forest	NUST	IEEE Internet Things J.	2022	VPRF⁹¹	[PUB]
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems	CNU	IEEE Access	2022		[PUB] [PDF]
BOFRF: A Novel Boosting-Based Federated Random Forest Algorithm on Horizontally Partitioned Data	METU	IEEE Access	2022	BOFRF⁹²	[PUB]
eFL-Boost: Efficient Federated Learning for Gradient Boosting Decision Trees	kobe-u	IEEE Access	2022	eFL-Boost⁹³	[PUB]
An Efficient Learning Framework for Federated XGBoost Using Secret Sharing and Distributed Optimization	TJU	ACM Trans. Intell. Syst. Technol.	2022	MP-FedXGB⁹⁴	[PUB] [PDF] [CODE]
An optional splitting extraction based gain-AUPRC balanced strategy in federated XGBoost for mitigating imbalanced credit card fraud detection	Swinburne University of Technology	Int. J. Bio Inspired Comput.	2022		[PUB]
Random Forest Based on Federated Learning for Intrusion Detection	Malardalen University	AIAI	2022	FL-RF⁹⁵	[PUB]
Cross-silo federated learning based decision trees	ETH Zürich	SAC	2022	FL-DT⁹⁶	[PUB]
Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning	Missouri S&T	INFCOM Workshops	2022	FL-ST³¹	[PUB]
VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	PKU	SIGMOD 🎓	2021	VF2Boost⁹⁷	[PUB]
Boosting with Multiple Sources	Google	NeurIPS🎓	2021		[PUB]
SecureBoost: A Lossless Federated Learning Framework 🔥	UC	IEEE Intell. Syst.	2021	SecureBoost⁹⁸	[PUB] [PDF] [SLIDE] [CODE] [解读] [UC]
A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System	CNU	IEEE Access	2021	BFF-IDS⁹⁹	[PUB]
Research on privacy protection of multi source data based on improved gbdt federated ensemble method with different metrics	NCUT	Phys. Commun.	2021	I-GBDT¹⁰⁰	[PUB]
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Vertical Federated Learning	UCAS; CAS	IEEE BigData	2021	Fed-EINI¹⁰¹	[PUB] [PDF]
Gradient Boosting Forest: a Two-Stage Ensemble Method Enabling Federated Learning of GBDTs	THU	ICONIP	2021	GBF-Cen¹⁰²	[PUB]
A k-Anonymised Federated Learning Framework with Decision Trees	Umeå University	DPM/CBT @ESORICS	2021	KA-FL¹⁰³	[PUB]
AF-DNDF: Asynchronous Federated Learning of Deep Neural Decision Forests	Chalmers	SEAA	2021	AF-DNDF¹⁰⁴	[PUB]
Compression Boosts Differentially Private Federated Learning	Univ. Grenoble Alpes	EuroS&P	2021	CB-DP¹⁰⁵	[PUB] [PDF]
Practical Federated Gradient Boosting Decision Trees	NUS; UWA	AAAI 🎓	2020	SimFL¹⁰⁶	[PUB] [PDF] [CODE]
Privacy Preserving Vertical Federated Learning for Tree-based Models	NUS	VLDB 🎓	2020	Pivot-DT¹⁰⁷	[PUB] [PDF] [VIDEO] [CODE]
Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing	Xidian University	ICDCS	2020	FEDXGB¹⁰⁸	[PUB] [PDF]
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling	University of Utah	IEEE BigData	2020	FedCluster¹⁰⁹	[PUB] [PDF]
New Approaches to Federated XGBoost Learning for Privacy-Preserving Data Analysis	kobe-u	ICONIP	2020	FL-XGBoost¹¹⁰	[PUB]
Bandwidth Slicing to Boost Federated Learning Over Passive Optical Networks	Chalmers University of Technology	IEEE Communications Letters	2020	FL-PON¹¹¹	[PUB]
DFedForest: Decentralized Federated Forest	UFRJ	Blockchain	2020	DFedForest¹¹²	[PUB]
Straggler Remission for Federated Learning via Decentralized Redundant Cayley Tree	Stevens Institute of Technology	LATINCOM	2020	DRC-tree¹¹³	[PUB]
Federated Soft Gradient Boosting Machine for Streaming Data	Sinovation Ventures AI Institute	Federated Learning	2020	Fed-sGBM¹¹⁴	[PUB] [解读]
Federated Learning of Deep Neural Decision Forests	Fraunhofer-Chalmers Centre	LOD	2019	FL-DNDF¹¹⁵	[PUB]
GTV: Generating Tabular Data via Vertical Federated Learning		preprint	2023		[PDF]
Federated Survival Forests		preprint	2023		[PDF]
Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data	HIT	preprint	2022	Fed-TDA¹¹⁶	[PDF]
Data Leakage in Tabular Federated Learning	ETH Zurich	preprint	2022	TabLeak¹¹⁷	[PDF]
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources		preprint	2022		[PDF]
Federated XGBoost on Sample-Wise Non-IID Data		preprint	2022		[PDF]
Hercules: Boosting the Performance of Privacy-preserving Federated Learning		preprint	2022	Hercules¹¹⁸	[PDF]
FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging		preprint	2022	FedGBF¹¹⁹	[PDF]
A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction.	THU	preprint	2022	HFL-XGBoost¹²⁰	[PDF]
An Efficient and Robust System for Vertically Federated Random Forest		preprint	2022		[PDF]
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost.	BUAA	preprint	2021	EBHE-VFXGB¹²¹	[PDF]
Guess what? You can boost Federated Learning for free		preprint	2021		[PDF]
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning 🔥		preprint	2021	SecureBoost+¹²²	[PDF] [CODE]
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data		preprint	2021	Fed-TGAN¹²³	[PDF]
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning	TUM	preprint	2021	FedXGBoost¹²⁴	[PDF]
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning		preprint	2020		[PDF]
FederBoost: Private Federated Learning for GBDT	ZJU	preprint	2020	FederBoost¹²⁵	[PDF]
Privacy Preserving Text Recognition with Gradient-Boosting for Federated Learning		preprint	2020		[PDF] [CODE]
Cloud-based Federated Boosting for Mobile Crowdsensing		preprint	2020		[ARXIV]
Federated Extra-Trees with Privacy Preserving		preprint	2020		[PDF]
Bandwidth Slicing to Boost Federated Learning in Edge Computing		preprint	2019		[PDF]
Revocable Federated Learning: A Benchmark of Federated Forest		preprint	2019		[PDF]
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost	CUHK	preprint	2019	F-XGBoost¹²⁶	[PDF] [CODE]

fl in top-tier journal

List of papers in the field of federated learning in Nature(and its sub-journals), Cell, Science(and Science Advances) and PANS refers to WOS search engine.

Title	Affiliation	Venue	Year	TL;DR	Materials
Federated machine learning in data-protection-compliant research	University of Hamburg	Nat. Mach. Intell.(Comment)	2023		[PUB]
Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer	Owkin	Nat. Med.	2023		[PUB] [CODE]
Federated learning enables big data for rare cancer boundary detection	University of Pennsylvania	Nat. Commun.	2022		[PUB] [PDF] [CODE]
Federated learning and Indigenous genomic data sovereignty	Hugging Face	Nat. Mach. Intell. (Comment)	2022		[PUB]
Federated disentangled representation learning for unsupervised brain anomaly detection	TUM	Nat. Mach. Intell.	2022	FedDis¹²⁷	[PUB] [PDF] [CODE]
Shifting machine learning for healthcare from development to deployment and from models to data		Nat. Biomed. Eng. (Review Article)	2022	FL-healthy¹²⁸	[PUB]
A federated graph neural network framework for privacy-preserving personalization	THU	Nat. Commun.	2022	FedPerGNN¹⁸	[PUB] [CODE] [解读]
Communication-efficient federated learning via knowledge distillation		Nat. Commun.	2022		[PUB] [PDF] [CODE]
Lead federated neuromorphic learning for wireless edge artificial intelligence		Nat. Commun.	2022		[PUB] [CODE] [解读]
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence		Nat. Mach. Intell.	2021		[PUB] [PDF] [CODE]
Federated learning for predicting clinical outcomes in patients with COVID-19		Nat. Med.	2021		[PUB] [CODE]
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning		Nat. Mach. Intell.(Perspective)	2021		[PUB]
Swarm Learning for decentralized and confidential clinical machine learning ⭐		Nature 🎓	2021		[PUB] [CODE] [SOFTWARE] [解读]
End-to-end privacy preserving deep learning on multi-institutional medical imaging		Nat. Mach. Intell.	2021		[PUB] [CODE] [解读]
Communication-efficient federated learning		PANS.	2021		[PUB] [CODE]
Breaking medical data sharing boundaries by using synthesized radiographs		Science. Advances.	2020		[PUB] [CODE]
Secure, privacy-preserving and federated machine learning in medical imaging ⭐		Nat. Mach. Intell.(Perspective)	2020		[PUB]

fl in top ai conference and journal

In this section, we will summarize Federated Learning papers accepted by top AI(Artificial Intelligence) conference and journal, Including IJCAI(International Joint Conference on Artificial Intelligence), AAAI(AAAI Conference on Artificial Intelligence), AISTATS(Artificial Intelligence and Statistics), AI(Artificial Intelligence).

Title	Affiliation	Venue	Year	TL;DR	Materials
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing	UTS	AAAI	2023	FedStar³	[PDF] [CODE]
FedGS: Federated Graph-based Sampling with Arbitrary Client Availability	XMU	AAAI	2023	FedGS⁴	[PDF] [CODE]
Incentive-boosted Federated Crowdsourcing	SDU	AAAI	2023	iFedCrowd⁸³	[PDF]
Towards Understanding Biased Client Selection in Federated Learning.	CMU	AISTATS	2022		[PUB] [CODE]
FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning	KAUST	AISTATS	2022	FLIX¹²⁹	[PUB] [PDF] [CODE]
Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective.	Stanford	AISTATS	2022		[PUB] [PDF] [CODE]
Federated Reinforcement Learning with Environment Heterogeneity.	PKU	AISTATS	2022		[PUB] [PDF] [CODE]
Federated Myopic Community Detection with One-shot Communication	Purdue	AISTATS	2022		[PUB] [PDF]
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits.	University of Virginia	AISTATS	2022		[PUB] [PDF] [CODE]
Towards Federated Bayesian Network Structure Learning with Continuous Optimization.	CMU	AISTATS	2022		[PUB] [PDF] [CODE]
Federated Learning with Buffered Asynchronous Aggregation	Meta AI	AISTATS	2022		[PUB] [PDF] [VIDEO]
Differentially Private Federated Learning on Heterogeneous Data.	Stanford	AISTATS	2022	DP-SCAFFOLD¹³⁰	[PUB] [PDF] [CODE]
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification	Princeton	AISTATS	2022	SparseFed¹³¹	[PUB] [PDF] [CODE] [VIDEO]
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning	KAUST	AISTATS	2022		[PUB] [PDF]
Federated Functional Gradient Boosting.	University of Pennsylvania	AISTATS	2022		[PUB] [PDF] [CODE]
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning.	Criteo AI Lab	AISTATS	2022	QLSD¹³²	[PUB] [PDF] [CODE] [VIDEO]
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting `kg.`	ZJU	IJCAI	2022	MaKEr⁹	[PUB] [PDF] [CODE]
Personalized Federated Learning With a Graph	UTS	IJCAI	2022	SFL¹⁰	[PUB] [PDF] [CODE]
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification	ZJU	IJCAI	2022	VFGNN¹¹	[PUB] [PDF]
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning		IJCAI	2022		[PUB] [PDF] [CODE]
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning		IJCAI	2022	Fed-ET¹³³	[PUB] [PDF]
Private Semi-Supervised Federated Learning.		IJCAI	2022		[PUB]
Continual Federated Learning Based on Knowledge Distillation.		IJCAI	2022		[PUB]
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features		IJCAI	2022	CReFF¹³⁴	[PUB] [PDF] [CODE]
Federated Multi-Task Attention for Cross-Individual Human Activity Recognition		IJCAI	2022		[PUB]
Personalized Federated Learning with Contextualized Generalization.		IJCAI	2022		[PUB] [PDF]
Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection.		IJCAI	2022		[PUB] [PDF]
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning		IJCAI	2022	FedCG¹³⁵	[PUB] [PDF] [CODE]
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server.		IJCAI	2022	FedDUAP¹³⁶	[PUB] [PDF]
Towards Verifiable Federated Learning `surv.`		IJCAI	2022		[PUB] [PDF]
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images	CUHK; BUAA	AAAI	2022		[PUB] [PDF] [CODE] [解读]
Federated Learning for Face Recognition with Gradient Correction	BUPT	AAAI	2022		[PUB] [PDF]
SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data	USC	AAAI	2022	SpreadGNN¹²	[PUB] [PDF] [CODE] [解读]
SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures	HIT; PCL	AAAI	2022	SmartIdx¹³⁷	[PUB] [CODE]
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network	TJU	AAAI	2022		[PUB] [PDF]
Seizing Critical Learning Periods in Federated Learning	SUNY-Binghamton University	AAAI	2022	FedFIM¹³⁸	[PUB] [PDF]
Coordinating Momenta for Cross-silo Federated Learning	University of Pittsburgh	AAAI	2022		[PUB] [PDF]
FedProto: Federated Prototype Learning over Heterogeneous Devices	UTS	AAAI	2022	FedProto¹³⁹	[PUB] [PDF] [CODE]
FedSoft: Soft Clustered Federated Learning with Proximal Local Updating	CMU	AAAI	2022	FedSoft¹⁴⁰	[PUB] [PDF] [CODE]
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better	The University of Texas at Austin	AAAI	2022		[PUB] [PDF] [CODE]
FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition	National Taiwan University	AAAI	2022	FedFR¹⁴¹	[PUB] [PDF] [CODE]
SplitFed: When Federated Learning Meets Split Learning	CSIRO	AAAI	2022	SplitFed¹⁴²	[PUB] [PDF] [CODE]
Efficient Device Scheduling with Multi-Job Federated Learning	Soochow University	AAAI	2022		[PUB] [PDF]
Implicit Gradient Alignment in Distributed and Federated Learning	IIT Kanpur	AAAI	2022		[PUB] [PDF]
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies	IBM Research	AAAI	2022	FlyNNFL¹⁴³	[PUB] [PDF] [CODE]
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization		IJCAI	2021		[PUB] [PDF] [VIDEO]
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning		IJCAI	2021		[PUB] [PDF]
FedSpeech: Federated Text-to-Speech with Continual Learning		IJCAI	2021	FedSpeech¹⁴⁴	[PUB] [PDF]
Practical One-Shot Federated Learning for Cross-Silo Setting		IJCAI	2021	FedKT¹⁴⁵	[PUB] [PDF] [CODE]
Federated Model Distillation with Noise-Free Differential Privacy		IJCAI	2021	FEDMD-NFDP¹⁴⁶	[PUB] [PDF] [VIDEO]
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy		IJCAI	2021	LDP-FL¹⁴⁷	[PUB] [PDF]
Federated Learning with Fair Averaging. 🔥		IJCAI	2021	FedFV¹⁴⁸	[PUB] [PDF] [CODE]
H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning.		IJCAI	2021	H-FL¹⁴⁹	[PUB] [PDF]
Communication-efficient and Scalable Decentralized Federated Edge Learning.		IJCAI	2021		[PUB]
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating	Xidian University; JD Tech	AAAI	2021		[PUB] [PDF] [VIDEO]
FedRec++: Lossless Federated Recommendation with Explicit Feedback	SZU	AAAI	2021	FedRec++¹⁵⁰	[PUB] [VIDEO]
Federated Multi-Armed Bandits	University of Virginia	AAAI	2021		[PUB] [PDF] [CODE] [VIDEO]
On the Convergence of Communication-Efficient Local SGD for Federated Learning	Temple University; University of Pittsburgh	AAAI	2021		[PUB] [VIDEO]
FLAME: Differentially Private Federated Learning in the Shuffle Model	Renmin University of China; Kyoto University	AAAI	2021	FLAME_D¹⁵¹	[PUB] [PDF] [VIDEO] [CODE]
Toward Understanding the Influence of Individual Clients in Federated Learning	SJTU; The University of Texas at Dallas	AAAI	2021		[PUB] [PDF] [VIDEO]
Provably Secure Federated Learning against Malicious Clients	Duke University	AAAI	2021		[PUB] [PDF] [VIDEO] [SLIDE]
Personalized Cross-Silo Federated Learning on Non-IID Data	Simon Fraser University; McMaster University	AAAI	2021	FedAMP¹⁵²	[PUB] [PDF] [VIDEO] [UC.]
Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation	Cornell University	AAAI	2021		[PUB] [PDF] [CODE] [VIDEO]
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning	University of Nevada; IBM Research	AAAI	2021		[PUB] [PDF] [VIDEO]
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning	IIT Bombay; IBM Research	AAAI	2021		[PUB] [PDF] [CODE] [VIDEO] [SUPPLEMENTARY]
Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models	CUHK; Arizona State University	AAAI	2021		[PUB] [PDF] [VIDEO] [CODE]
Addressing Class Imbalance in Federated Learning	Northwestern University	AAAI	2021		[PUB] [PDF] [VIDEO] [CODE] [解读]
Defending against Backdoors in Federated Learning with Robust Learning Rate	The University of Texas at Dallas	AAAI	2021		[PUB] [PDF] [VIDEO] [CODE]
Free-rider Attacks on Model Aggregation in Federated Learning	Accenture Labs	AISTATS	2021		[PUB] [PDF] [CODE] [VIDEO] [SUPPLEMENTARY]
Federated f-differential privacy	University of Pennsylvania	AISTATS	2021		[PUB] [CODE] [VIDEO] [SUPPLEMENTARY]
Federated learning with compression: Unified analysis and sharp guarantees 🔥	The Pennsylvania State University; The University of Texas at Austin	AISTATS	2021		[PUB] [PDF] [CODE] [VIDEO] [SUPPLEMENTARY]
Shuffled Model of Differential Privacy in Federated Learning	UCLA; Google	AISTATS	2021		[PUB] [VIDEO] [SUPPLEMENTARY]
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning	Google	AISTATS	2021		[PUB] [PDF] [VIDEO] [SUPPLEMENTARY]
Federated Multi-armed Bandits with Personalization	University of Virginia; The Pennsylvania State University	AISTATS	2021		[PUB] [PDF] [CODE] [VIDEO] [SUPPLEMENTARY]
Towards Flexible Device Participation in Federated Learning	CMU; SYSU	AISTATS	2021		[PUB] [PDF] [VIDEO] [SUPPLEMENTARY]
Federated Meta-Learning for Fraudulent Credit Card Detection		IJCAI	2020		[PUB] [VIDEO]
A Multi-player Game for Studying Federated Learning Incentive Schemes		IJCAI	2020	FedGame¹⁵³	[PUB] [CODE] [解读]
Practical Federated Gradient Boosting Decision Trees	NUS; UWA	AAAI	2020	SimFL¹⁰⁶	[PUB] [PDF] [CODE]
Federated Learning for Vision-and-Language Grounding Problems	PKU; Tencent	AAAI	2020		[PUB]
Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework	BUAA	AAAI	2020		[PUB]
Federated Patient Hashing	Cornell University	AAAI	2020		[PUB]
Robust Federated Learning via Collaborative Machine Teaching	Symantec Research Labs; KAUST	AAAI	2020		[PUB] [PDF]
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning	WeBank	AAAI	2020		[PUB] [PDF] [CODE]
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization	UC Santa Barbara; UT Austin	AISTATS	2020		[PUB] [PDF] [VIDEO] [SUPPLEMENTARY]
How To Backdoor Federated Learning 🔥	Cornell Tech	AISTATS	2020		[PUB] [PDF] [VIDEO] [CODE] [SUPPLEMENTARY]
Federated Heavy Hitters Discovery with Differential Privacy	RPI; Google	AISTATS	2020		[PUB] [PDF] [VIDEO] [SUPPLEMENTARY]
Multi-Agent Visualization for Explaining Federated Learning	WeBank	IJCAI	2019		[PUB] [VIDEO]

fl in top ml conference and journal

In this section, we will summarize Federated Learning papers accepted by top ML(machine learning) conference and journal, Including NeurIPS(Annual Conference on Neural Information Processing Systems), ICML(International Conference on Machine Learning), ICLR(International Conference on Learning Representations), COLT(Annual Conference Computational Learning Theory) , UAI(Conference on Uncertainty in Artificial Intelligence), JMLR(Journal of Machine Learning Research), TPAMI(IEEE Transactions on Pattern Analysis and Machine Intelligence).

NeurIPS 2022(OpenReview), 2021(OpenReview), 2020, 2018, 2017
ICML 2022, 2021, 2020, 2019
ICLR 2023, 2022, 2021, 2020
COLT NULL
UAI 2022, 2021
JMLR 2021
TPAMI 2023, 2022

Title	Affiliation	Venue	Year	TL;DR	Materials
FedIPR: Ownership Verification for Federated Deep Neural Network Models	SJTU	TPAMI	2023		[PUB] [PDF] [CODE] [解读]
Decentralized Federated Averaging	NUDT	TPAMI	2023		[PUB] [PDF]
Personalized Federated Learning with Feature Alignment and Classifier Collaboration	THU	ICLR	2023		[PUB] [CODE]
MocoSFL: enabling cross-client collaborative self-supervised learning	ASU	ICLR	2023		[PUB] [CODE]
Single-shot General Hyper-parameter Optimization for Federated Learning	IBM	ICLR	2023		[PUB] [PDF] [CODE]
Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated	Facebook	ICLR	2023		[PUB] [PDF] [CODE]
FedExP: Speeding up Federated Averaging via Extrapolation	CMU	ICLR	2023		[PUB] [PDF] [CODE]
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection	MSU	ICLR	2023		[PUB] [CODE]
DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity	KAUST	ICLR	2023		[PUB] [PDF] [CODE]
Machine Unlearning of Federated Clusters	University of Illinois	ICLR	2023		[PUB] [PDF] [CODE]
Federated Neural Bandits	NUS	ICLR	2023		[PUB] [PDF] [CODE]
FedFA: Federated Feature Augmentation	ETH Zurich	ICLR	2023		[PUB] [PDF] [CODE]
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach	CMU	ICLR	2023		[PUB] [PDF] [CODE]
Better Generative Replay for Continual Federated Learning	University of Virginia	ICLR	2023		[PUB] [CODE]
Federated Learning from Small Datasets	IKIM	ICLR	2023		[PUB] [PDF]
Federated Nearest Neighbor Machine Translation	USTC	ICLR	2023		[PUB] [PDF]
Meta Knowledge Condensation for Federated Learning	A*STAR	ICLR	2023		[PUB] [PDF]
Test-Time Robust Personalization for Federated Learning	EPFL	ICLR	2023		[PUB] [PDF] [CODE]
DepthFL : Depthwise Federated Learning for Heterogeneous Clients	SNU	ICLR	2023		[PUB]
Towards Addressing Label Skews in One-Shot Federated Learning	NUS	ICLR	2023		[PUB] [CODE]
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning	NUS	ICLR	2023		[PUB] [PDF] [CODE]
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation	UMD	ICLR	2023		[PUB] [CODE]
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication	UMD	ICLR	2023		[PUB] [PDF] [CODE]
Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses	USC	ICLR	2023		[PUB] [PDF] [CODE]
Effective passive membership inference attacks in federated learning against overparameterized models	Purdue University	ICLR	2023		[PUB]
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification	University of Cambridge	ICLR	2023		[PUB] [PDF] [CODE]
Multimodal Federated Learning via Contrastive Representation Ensemble	THU	ICLR	2023		[PUB] [PDF] [CODE]
Faster federated optimization under second-order similarity	Princeton University	ICLR	2023		[PUB] [PDF] [CODE]
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy	University of Sydney	ICLR	2023		[PUB] [CODE]
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation	utexas	ICLR	2023		[PUB] [PDF] [CODE]
PerFedMask: Personalized Federated Learning with Optimized Masking Vectors	UBC	ICLR	2023		[PUB] [CODE]
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data	GMU	ICLR	2023		[PUB] [CODE]
FedDAR: Federated Domain-Aware Representation Learning	Harvard	ICLR	2023		[PUB] [PDF] [CODE]
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning	upenn	ICLR	2023		[PUB] [CODE]
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning	Purdue University	ICLR	2023		[PUB] [PDF] [CODE]
Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses	RUC	ICLR	2023		[PUB]
Efficient Federated Domain Translation	Purdue University	ICLR	2023		[PUB] [CODE]
On the Importance and Applicability of Pre-Training for Federated Learning	OSU	ICLR	2023		[PUB] [PDF] [CODE]
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models	UMD	ICLR	2023		[PUB] [PDF] [CODE]
A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy	UCLA	ICLR	2023		[PUB] [PDF]
Instance-wise Batch Label Restoration via Gradients in Federated Learning	BUAA	ICLR	2023		[PUB] [CODE]
Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity	College of William and Mary	ICLR	2023		[PUB]
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning	University of Warwick	ICLR	2023		[PUB] [PDF] [CODE]
Sparse Random Networks for Communication-Efficient Federated Learning	Stanford	ICLR	2023		[PUB] [PDF] [CODE]
Combating Exacerbated Heterogeneity for Robust Decentralized Models	HKBU	ICLR	2023		[PUB] [CODE]
Hyperparameter Optimization through Neural Network Partitioning	University of Cambridge	ICLR	2023		[PUB] [PDF]
Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision?	MIT	ICLR	2023		[PUB] [PDF] [CODE]
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top	mbzuai	ICLR	2023		[PUB] [PDF] [CODE]
Dual Diffusion Implicit Bridges for Image-to-Image Translation	Stanford	ICLR	2023		[PUB] [PDF] [CODE]
Federated online clustering of bandits.	CUHK	UAI	2022		[PUB] [PDF] [CODE]
Privacy-aware compression for federated data analysis.	Meta AI	UAI	2022		[PUB] [PDF] [CODE]
Faster non-convex federated learning via global and local momentum.	UTEXAS	UAI	2022		[PUB] [PDF]
Fedvarp: Tackling the variance due to partial client participation in federated learning.	CMU	UAI	2022		[PUB] [PDF]
SASH: Efficient secure aggregation based on SHPRG for federated learning	CAS; CASTEST	UAI	2022		[PUB] [PDF]
Bayesian federated estimation of causal effects from observational data	NUS	UAI	2022		[PUB] [PDF]
Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning	Hanyang University	TPAMI	2022		[PUB]
Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning	ZJU	TPAMI	2022	TPAMI-LAQ¹⁵⁴	[PUB] [CODE]
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox	Moscow Institute of Physics and Technology	NeurIPS	2022		[PUB] [PDF]
LAMP: Extracting Text from Gradients with Language Model Priors	ETHZ	NeurIPS	2022		[PUB] [CODE]
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning	utexas	NeurIPS	2022		[PUB] [PDF]
On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond	NUIST	NeurIPS	2022		[PUB] [PDF]
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams	WISC	NeurIPS	2022		[PUB] [CODE]
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks	Columbia University	NeurIPS	2022		[PUB] [PDF]
Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective	PKU	NeurIPS	2022		[PUB]
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise	Stanford	NeurIPS	2022		[PUB] [PDF]
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization	KAUST	NeurIPS	2022		[PUB] [PDF]
On-Demand Sampling: Learning Optimally from Multiple Distributions	UC Berkeley	NeurIPS	2022		[PUB] [CODE]
Improved Utility Analysis of Private CountSketch	ITU	NeurIPS	2022		[PUB] [PDF] [CODE]
Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning	HUAWEI	NeurIPS	2022		[PUB] [CODE]
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities	phystech	NeurIPS	2022		[PUB] [PDF]
BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression	Princeton	NeurIPS	2022		[PUB] [PDF] [CODE]
Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning	The University of Tokyo	NeurIPS	2022		[PUB] [PDF]
Near-Optimal Collaborative Learning in Bandits	INRIA; Inserm	NeurIPS	2022		[PUB] [PDF] [CODE]
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees	phystech	NeurIPS	2022		[PUB] [PDF]
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization	TTIC	NeurIPS	2022		[PUB] [CODE]
FedPop: A Bayesian Approach for Personalised Federated Learning	Skoltech	NeurIPS	2022	FedPop¹⁵⁵	[PUB] [PDF]
Fairness in Federated Learning via Core-Stability	UIUC	NeurIPS	2022	CoreFed¹⁵⁶	[PUB] [CODE]
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning	Sorbonne Université	NeurIPS	2022	SecureFedYJ¹⁵⁷	[PUB] [PDF]
FedRolex: Model-Heterogeneous Federated Learning with Rolling Submodel Extraction	MSU	NeurIPS	2022	FedRolex¹⁵⁸	[PUB] [CODE]
On Sample Optimality in Personalized Collaborative and Federated Learning	INRIA	NeurIPS	2022		[PUB]
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing	HKUST	NeurIPS	2022	DReS-FL¹⁵⁹	[PUB] [PDF]
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning	THU	NeurIPS	2022	FairVFL¹⁶⁰	[PUB]
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning	KAUST	NeurIPS	2022	VR-ProxSkip¹⁶¹	[PUB] [PDF]
VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?	WHU	NeurIPS	2022	VF-PS¹⁶²	[PUB] [CODE]
DENSE: Data-Free One-Shot Federated Learning	ZJU	NeurIPS	2022	DENSE¹⁶³	[PUB] [PDF]
CalFAT: Calibrated Federated Adversarial Training with Label Skewness	ZJU	NeurIPS	2022	CalFAT¹⁶⁴	[PUB] [PDF]
SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max Learning	OSU	NeurIPS	2022	SAGDA¹⁶⁵	[PUB] [PDF]
Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning	OSU	NeurIPS	2022	FAT-Clipping¹⁶⁶	[PUB] [PDF]
Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness	PKU	NeurIPS	2022		[PUB]
Federated Submodel Optimization for Hot and Cold Data Features	SJTU	NeurIPS	2022	FedSubAvg¹⁶⁷	[PUB]
BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels	UC Berkeley	NeurIPS	2022	BooNTK¹⁶⁸	[PUB] [PDF]
Byzantine-tolerant federated Gaussian process regression for streaming data	PSU	NeurIPS	2022		[PUB] [CODE]
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression	CMU	NeurIPS	2022	SoteriaFL¹⁶⁹	[PUB] [PDF]
Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering	Yale	NeurIPS	2022		[PUB] [PDF] [CODE]
Communication Efficient Federated Learning for Generalized Linear Bandits	University of Virginia	NeurIPS	2022		[PUB] [CODE]
Recovering Private Text in Federated Learning of Language Models	Princeton	NeurIPS	2022	FILM¹⁷⁰	[PUB] [PDF] [CODE]
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach	UTS	NeurIPS	2022	FedPCL¹⁷¹	[PUB] [PDF]
Global Convergence of Federated Learning for Mixed Regression	Northeastern University	NeurIPS	2022		[PUB] [PDF]
Resource-Adaptive Federated Learning with All-In-One Neural Composition	JHU	NeurIPS	2022	FLANC¹⁷²	[PUB]
Self-Aware Personalized Federated Learning	Amazon	NeurIPS	2022	Self-FL¹⁷³	[PUB] [PDF]
A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning	Northeastern University	NeurIPS	2022	FedGDA-GT¹⁷⁴	[PUB] [PDF]
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects	NUS	NeurIPS	2022		[PUB]
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning	EPFL	NeurIPS	2022		[PUB] [PDF]
Personalized Online Federated Multi-Kernel Learning	UCI	NeurIPS	2022		[PUB]
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training	Duke University	NeurIPS	2022	SemiFL¹⁷⁵	[PUB] [PDF] [CODE]
A Unified Analysis of Federated Learning with Arbitrary Client Participation	IBM	NeurIPS	2022		[PUB] [PDF]
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning	KAIST	NeurIPS	2022	FedNTD¹⁷⁶	[PUB] [PDF] [CODE]
FedSR: A Simple and Effective Domain Generalization Method for Federated Learning	University of Oxford	NeurIPS	2022	FedSR¹⁷⁷	[PUB] [CODE]
Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching	KAIST	NeurIPS	2022	Factorized-FL¹⁷⁸	[PUB] [PDF] [CODE]
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits	UC	NeurIPS	2022	FedLinUCB¹⁷⁹	[PUB] [PDF]
Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework	Tulane University	NeurIPS	2022		[PUB]
On Privacy and Personalization in Cross-Silo Federated Learning	CMU	NeurIPS	2022		[PUB] [PDF]
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning	NUS	NeurIPS	2022	FedSim¹⁸⁰	[PUB] [PDF] [CODE]
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings	Owkin	NeurIPS Datasets and Benchmarks	2022		[PUB] [CODE]
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources	University of Pittsburgh	ICML	2022		[PUB] [PDF] [CODE]
Fast Composite Optimization and Statistical Recovery in Federated Learning	SJTU	ICML	2022		[PUB] [PDF] [CODE]
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning	NYU	ICML	2022	PPSGD¹⁸¹	[PUB] [PDF] [CODE]
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning 🔥	Stanford; Google Research	ICML	2022		[PUB] [PDF] [CODE] [SLIDE]
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation	Stanford; Google Research	ICML	2022	PBM¹⁸²	[PUB] [PDF] [CODE]
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training	USTC	ICML	2022	DisPFL¹⁸³	[PUB] [PDF] [CODE]
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning	University of Oulu	ICML	2022	FedNew¹⁸⁴	[PUB] [PDF] [CODE]
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning	University of Cambridge	ICML	2022	DAdaQuant¹⁸⁵	[PUB] [PDF] [SLIDE] [CODE]
Accelerated Federated Learning with Decoupled Adaptive Optimization	Auburn University	ICML	2022		[PUB] [PDF]
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling	Georgia Tech	ICML	2022		[PUB] [PDF]
Multi-Level Branched Regularization for Federated Learning	Seoul National University	ICML	2022	FedMLB¹⁸⁶	[PUB] [PDF] [CODE] [PAGE]
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale 🔥	University of Michigan	ICML	2022	FedScale¹⁸⁷	[PUB] [PDF] [CODE]
Federated Learning with Positive and Unlabeled Data	XJTU	ICML	2022	FedPU¹⁸⁸	[PUB] [PDF] [CODE]
Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning	SJTU	ICML	2022		[PUB] [CODE]
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering	University of Michigan	ICML	2022	Orchestra¹⁸⁹	[PUB] [PDF] [CODE]
Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring	USTC	ICML	2022	DFL¹⁹⁰	[PUB] [PDF] [CODE] [SLIDE] [解读]
Architecture Agnostic Federated Learning for Neural Networks	The University of Texas at Austin	ICML	2022	FedHeNN¹⁹¹	[PUB] [PDF] [SLIDE]
Personalized Federated Learning through Local Memorization	Inria	ICML	2022	KNN-PER¹⁹²	[PUB] [PDF] [CODE]
Proximal and Federated Random Reshuffling	KAUST	ICML	2022	ProxRR¹⁹³	[PUB] [PDF] [CODE]
Federated Learning with Partial Model Personalization	University of Washington	ICML	2022		[PUB] [PDF] [CODE]
Generalized Federated Learning via Sharpness Aware Minimization	University of South Florida	ICML	2022		[PUB] [PDF]
FedNL: Making Newton-Type Methods Applicable to Federated Learning	KAUST	ICML	2022	FedNL¹⁹⁴	[PUB] [PDF] [VIDEO] [SLIDE]
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms	CMU	ICML	2022		[PUB] [PDF] [SLIDE]
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning	Hong Kong Baptist University	ICML	2022	VFL¹⁹⁵	[PUB] [PDF] [CODE] [解读]
FedNest: Federated Bilevel, Minimax, and Compositional Optimization	University of Michigan	ICML	2022	FedNest¹⁹⁶	[PUB] [PDF] [CODE]
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning	VMware Research	ICML	2022	EDEN¹⁹⁷	[PUB] [PDF] [CODE]
Communication-Efficient Adaptive Federated Learning	Pennsylvania State University	ICML	2022		[PUB] [PDF]
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training	CISPA Helmholz Center for Information Security	ICML	2022	ProgFed¹⁹⁸	[PUB] [PDF] [SLIDE] [CODE]
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification 🔥	University of Maryland	ICML	2022	breaching¹⁹⁹	[PUB] [PDF] [CODE]
Anarchic Federated Learning	The Ohio State University	ICML	2022		[PUB] [PDF]
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning	Nankai University	ICML	2022	QSFL²⁰⁰	[PUB] [CODE]
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization	KAIST	ICML	2022		[PUB] [PDF]
Neural Tangent Kernel Empowered Federated Learning	NC State University	ICML	2022		[PUB] [PDF] [CODE]
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy	UMN	ICML	2022		[PUB] [PDF]
Personalized Federated Learning via Variational Bayesian Inference	CAS	ICML	2022		[PUB] [PDF] [SLIDE] [UC.]
Federated Learning with Label Distribution Skew via Logits Calibration	ZJU	ICML	2022		[PUB]
Neurotoxin: Durable Backdoors in Federated Learning	Southeast University;Princeton	ICML	2022	Neurotoxin²⁰¹	[PUB] [PDF] [CODE]
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems	Michigan State University	ICML	2022		[PUB]
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond	KAIST	ICLR (oral)	2022		[PUB] [CODE]
Bayesian Framework for Gradient Leakage	ETH Zurich	ICLR	2022		[PUB] [PDF] [CODE]
Federated Learning from only unlabeled data with class-conditional-sharing clients	The University of Tokyo; CUHK	ICLR	2022	FedUL²⁰²	[PUB] [CODE]
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning	CMU; University of Illinois at Urbana-Champaign; University of Washington	ICLR	2022	FedChain²⁰³	[PUB] [PDF]
Acceleration of Federated Learning with Alleviated Forgetting in Local Training	THU	ICLR	2022	FedReg²⁰⁴	[PUB] [PDF] [CODE]
FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated Learning	POSTECH	ICLR	2022		[PUB] [PDF] [CODE]
An Agnostic Approach to Federated Learning with Class Imbalance	University of Pennsylvania	ICLR	2022		[PUB] [CODE]
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization	Michigan State University; The University of Texas at Austin	ICLR	2022		[PUB] [PDF] [CODE]
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models 🔥	University of Maryland; NYU	ICLR	2022		[PUB] [PDF] [CODE]
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity	University of Cambridge; University of Oxford	ICLR	2022		[PUB] [PDF]
Diverse Client Selection for Federated Learning via Submodular Maximization	Intel; CMU	ICLR	2022		[PUB] [CODE]
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?	Purdue	ICLR	2022		[PUB] [PDF] [CODE]
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions 🔥	University of Maryland; Google	ICLR	2022		[PUB] [CODE]
Towards Model Agnostic Federated Learning Using Knowledge Distillation	EPFL	ICLR	2022		[PUB] [PDF] [CODE]
Divergence-aware Federated Self-Supervised Learning	NTU; SenseTime	ICLR	2022		[PUB] [PDF] [CODE]
What Do We Mean by Generalization in Federated Learning? 🔥	Stanford; Google	ICLR	2022		[PUB] [PDF] [CODE]
FedBABU: Toward Enhanced Representation for Federated Image Classification	KAIST	ICLR	2022		[PUB] [PDF] [CODE]
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing	EPFL	ICLR	2022		[PUB] [PDF] [CODE]
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters	Aibee	ICLR Spotlight	2022		[PUB] [PDF] [PAGE] [解读]
Hybrid Local SGD for Federated Learning with Heterogeneous Communications	University of Texas; Pennsylvania State University	ICLR	2022		[PUB]
On Bridging Generic and Personalized Federated Learning for Image Classification	The Ohio State University	ICLR	2022	Fed-RoD²⁰⁵	[PUB] [PDF] [CODE]
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond	KAIST; MIT	ICLR	2022		[PUB] [PDF]
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them.		JMLR	2021		[PUB] [CODE]
Constrained differentially private federated learning for low-bandwidth devices		UAI	2021		[PUB] [PDF]
Federated stochastic gradient Langevin dynamics		UAI	2021		[PUB] [PDF]
Federated Learning Based on Dynamic Regularization	BU; ARM	ICLR	2021		[PUB] [PDF] [CODE]
Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning	The Ohio State University	ICLR	2021		[PUB] [PDF]
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients	Duke University	ICLR	2021	HeteroFL²⁰⁶	[PUB] [PDF] [CODE]
FedMix: Approximation of Mixup under Mean Augmented Federated Learning	KAIST	ICLR	2021	FedMix²⁰⁷	[PUB] [PDF]
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms 🔥	CMU; Google	ICLR	2021		[PUB] [PDF] [CODE]
Adaptive Federated Optimization 🔥	Google	ICLR	2021		[PUB] [PDF] [CODE]
Personalized Federated Learning with First Order Model Optimization	Stanford; NVIDIA	ICLR	2021	FedFomo²⁰⁸	[PUB] [PDF] [CODE] [UC.]
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization 🔥	Princeton	ICLR	2021	FedBN²⁰⁹	[PUB] [PDF] [CODE]
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning	The Ohio State University	ICLR	2021	FedBE²¹⁰	[PUB] [PDF] [CODE]
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning	KAIST	ICLR	2021		[PUB] [PDF] [CODE]
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation	ZJU	ICML	2021		[PUB] [PDF] [CODE] [解读]
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix	Harvard University	ICML	2021		[PUB] [PDF] [VIDEO] [CODE]
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis	PKU; Princeton	ICML	2021	FL-NTK²¹¹	[PUB] [PDF] [VIDEO]
Personalized Federated Learning using Hypernetworks 🔥	Bar-Ilan University; NVIDIA	ICML	2021		[PUB] [PDF] [CODE] [PAGE] [VIDEO] [解读]
Federated Composite Optimization	Stanford; Google	ICML	2021		[PUB] [PDF] [CODE] [VIDEO] [SLIDE]
Exploiting Shared Representations for Personalized Federated Learning	University of Texas at Austin; University of Pennsylvania	ICML	2021		[PUB] [PDF] [CODE] [VIDEO]
Data-Free Knowledge Distillation for Heterogeneous Federated Learning 🔥	Michigan State University	ICML	2021		[PUB] [PDF] [CODE] [VIDEO]
Federated Continual Learning with Weighted Inter-client Transfer	KAIST	ICML	2021		[PUB] [PDF] [CODE] [VIDEO]
Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity	The University of Iowa	ICML	2021		[PUB] [PDF] [CODE] [VIDEO]
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning	The University of Tokyo	ICML	2021		[PUB] [PDF] [VIDEO]
Federated Learning of User Verification Models Without Sharing Embeddings	Qualcomm	ICML	2021		[PUB] [PDF] [VIDEO]
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning	Accenture	ICML	2021		[PUB] [PDF] [CODE] [VIDEO]
Ditto: Fair and Robust Federated Learning Through Personalization	CMU; Facebook AI	ICML	2021		[PUB] [PDF] [CODE] [VIDEO]
Heterogeneity for the Win: One-Shot Federated Clustering	CMU	ICML	2021		[PUB] [PDF] [VIDEO]
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation 🔥	Google	ICML	2021		[PUB] [PDF] [CODE] [VIDEO]
Debiasing Model Updates for Improving Personalized Federated Training	BU; Arm	ICML	2021		[PUB] [CODE] [VIDEO]
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning	Toyota; Berkeley; Cornell University	ICML	2021		[PUB] [PDF] [CODE] [VIDEO]
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks	UIUC; IBM	ICML	2021		[PUB] [PDF] [CODE] [VIDEO]
Federated Learning under Arbitrary Communication Patterns	Indiana University; Amazon	ICML	2021		[PUB] [VIDEO]
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression	CMU	NeurIPS	2021		[PUB] [PDF]
Boosting with Multiple Sources	Google	NeurIPS	2021		[PUB]
DRIVE: One-bit Distributed Mean Estimation	VMware	NeurIPS	2021		[PUB] [CODE]
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning	NUS	NeurIPS	2021		[PUB] [CODE]
Gradient Inversion with Generative Image Prior	POSTECH	NeurIPS	2021		[PUB] [PDF] [CODE]
Distributed Machine Learning with Sparse Heterogeneous Data	University of Oxford	NeurIPS	2021		[PUB] [PDF]
Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning	UCLA	NeurIPS	2021		[PUB] [PDF]
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries	KAIST	NeurIPS	2021	Sageflow²¹²	[PUB]
CAFE: Catastrophic Data Leakage in Vertical Federated Learning	Rensselaer Polytechnic Institute; IBM Research	NeurIPS	2021	CAFE²¹³	[PUB] [CODE]
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee	NUS	NeurIPS	2021		[PUB] [PDF] [CODE]
Optimality and Stability in Federated Learning: A Game-theoretic Approach	Cornell University	NeurIPS	2021		[PUB] [PDF] [CODE]
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning	UCLA	NeurIPS	2021	QuPeD²¹⁴	[PUB] [PDF] [CODE] [解读]
The Skellam Mechanism for Differentially Private Federated Learning 🔥	Google Research; CMU	NeurIPS	2021		[PUB] [PDF] [CODE]
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data	NUS; Huawei	NeurIPS	2021		[PUB] [PDF]
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning	UMN	NeurIPS	2021		[PUB] [PDF]
Subgraph Federated Learning with Missing Neighbor Generation	Emory; UBC; Lehigh University	NeurIPS	2021	FedSage⁴¹	[PUB] [PDF] [CODE] [解读]
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning 🔥	Princeton	NeurIPS	2021	GradAttack²¹⁵	[PUB] [PDF] [CODE]
Personalized Federated Learning With Gaussian Processes	Bar-Ilan University	NeurIPS	2021		[PUB] [PDF] [CODE]
Differentially Private Federated Bayesian Optimization with Distributed Exploration	MIT; NUS	NeurIPS	2021		[PUB] [PDF] [CODE]
Parameterized Knowledge Transfer for Personalized Federated Learning	PolyU	NeurIPS	2021	KT-pFL²¹⁶	[PUB] [PDF] [CODE]
Federated Reconstruction: Partially Local Federated Learning 🔥	Google Research	NeurIPS	2021		[PUB] [PDF] [CODE] [UC.]
Fast Federated Learning in the Presence of Arbitrary Device Unavailability	THU; Princeton; MIT	NeurIPS	2021		[PUB] [PDF] [CODE]
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective	Duke University; Accenture Labs	NeurIPS	2021	FL-WBC²¹⁷	[PUB] [PDF] [CODE]
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout	KAUST; Samsung AI Center	NeurIPS	2021	FjORD²¹⁸	[PUB] [PDF]
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients	University of Pennsylvania	NeurIPS	2021		[PUB] [PDF] [VIDEO]
Federated Multi-Task Learning under a Mixture of Distributions	INRIA; Accenture Labs	NeurIPS	2021		[PUB] [PDF] [CODE]
Federated Graph Classification over Non-IID Graphs	Emory	NeurIPS	2021	GCFL⁴⁰	[PUB] [PDF] [CODE] [解读]
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing	CMU; Hewlett Packard Enterprise	NeurIPS	2021	FedEx²¹⁹	[PUB] [PDF] [CODE]
On Large-Cohort Training for Federated Learning 🔥	Google; CMU	NeurIPS	2021	Large-Cohort²²⁰	[PUB] [PDF] [CODE]
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning	KAUST; Columbia University; University of Central Florida	NeurIPS	2021	DeepReduce²²¹	[PUB] [PDF] [CODE]
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization	Huawei	NeurIPS	2021	PartialFed²²²	[PUB] [VIDEO]
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis	KAIST	NeurIPS	2021		[PUB] [PDF]
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning	THU; Alibaba; Weill Cornell Medicine	NeurIPS	2021	FCFL²²³	[PUB] [PDF] [CODE]
Federated Linear Contextual Bandits	The Pennsylvania State University; Facebook; University of Virginia	NeurIPS	2021		[PUB] [PDF] [CODE]
Few-Round Learning for Federated Learning	KAIST	NeurIPS	2021		[PUB]
Breaking the centralized barrier for cross-device federated learning	EPFL; Google Research	NeurIPS	2021		[PUB] [CODE] [VIDEO]
Federated-EM with heterogeneity mitigation and variance reduction	Ecole Polytechnique; Google Research	NeurIPS	2021	Federated-EM²²⁴	[PUB] [PDF]
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning	MIT; Amazon; Google	NeurIPS	2021		[PUB] [PAGE] [SLIDE]
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization	University of North Carolina at Chapel Hill; IBM Research	NeurIPS	2021	FedDR²²⁵	[PUB] [PDF] [CODE]
Federated Adversarial Domain Adaptation	BU; Columbia University; Rutgers University	ICLR	2020		[PUB] [PDF] [CODE]
DBA: Distributed Backdoor Attacks against Federated Learning	ZJU; IBM Research	ICLR	2020		[PUB] [CODE]
Fair Resource Allocation in Federated Learning 🔥	CMU; Facebook AI	ICLR	2020	fair-flearn²²⁶	[PUB] [PDF] [CODE]
Federated Learning with Matched Averaging 🔥	University of Wisconsin-Madison; IBM Research	ICLR	2020	FedMA²²⁷	[PUB] [PDF] [CODE]
Differentially Private Meta-Learning	CMU	ICLR	2020		[PUB] [PDF]
Generative Models for Effective ML on Private, Decentralized Datasets 🔥	Google	ICLR	2020		[PUB] [PDF] [CODE]
On the Convergence of FedAvg on Non-IID Data 🔥	PKU	ICLR	2020		[PUB] [PDF] [CODE] [解读]
FedBoost: A Communication-Efficient Algorithm for Federated Learning	Google	ICML	2020	FedBoost²²⁸	[PUB] [VIDEO]
FetchSGD: Communication-Efficient Federated Learning with Sketching	UC Berkeley; Johns Hopkins University; Amazon	ICML	2020	FetchSGD²²⁹	[PUB] [PDF] [VIDEO] [CODE]
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning	EPFL; Google	ICML	2020	SCAFFOLD²³⁰	[PUB] [PDF] [VIDEO] [UC.] [解读]
Federated Learning with Only Positive Labels	Google	ICML	2020		[PUB] [PDF] [VIDEO]
From Local SGD to Local Fixed-Point Methods for Federated Learning	Moscow Institute of Physics and Technology; KAUST	ICML	2020		[PUB] [PDF] [SLIDE] [VIDEO]
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization	KAUST	ICML	2020		[PUB] [PDF] [SLIDE] [VIDEO]
Differentially-Private Federated Linear Bandits	MIT	NeurIPS	2020		[PUB] [PDF] [CODE]
Federated Principal Component Analysis	University of Cambridge; Quine Technologies	NeurIPS	2020		[PUB] [PDF] [CODE]
FedSplit: an algorithmic framework for fast federated optimization	UC Berkeley	NeurIPS	2020	FedSplit²³¹	[PUB] [PDF]
Federated Bayesian Optimization via Thompson Sampling	NUS; MIT	NeurIPS	2020	fbo²³²	[PUB] [PDF] [CODE]
Lower Bounds and Optimal Algorithms for Personalized Federated Learning	KAUST	NeurIPS	2020		[PUB] [PDF]
Robust Federated Learning: The Case of Affine Distribution Shifts	UC Santa Barbara; MIT	NeurIPS	2020	RobustFL²³³	[PUB] [PDF] [CODE]
An Efficient Framework for Clustered Federated Learning	UC Berkeley; DeepMind	NeurIPS	2020	ifca²³⁴	[PUB] [PDF] [CODE]
Distributionally Robust Federated Averaging 🔥	Pennsylvania State University	NeurIPS	2020	DRFA²³⁵	[PUB] [PDF] [CODE]
Personalized Federated Learning with Moreau Envelopes 🔥	The University of Sydney	NeurIPS	2020		[PUB] [PDF] [CODE]
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach	MIT; UT Austin	NeurIPS	2020	Per-FedAvg²³⁶	[PUB] [PDF] [UC.]
Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge	USC	NeurIPS	2020	FedGKT²³⁷	[PUB] [PDF] [CODE] [解读]
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization 🔥	CMU; Princeton	NeurIPS	2020	FedNova²³⁸	[PUB] [PDF] [CODE] [UC.]
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	University of Wisconsin-Madison	NeurIPS	2020		[PUB] [PDF]
Federated Accelerated Stochastic Gradient Descent	Stanford	NeurIPS	2020	FedAc²³⁹	[PUB] [PDF] [CODE] [VIDEO]
Inverting Gradients - How easy is it to break privacy in federated learning? 🔥	University of Siegen	NeurIPS	2020		[PUB] [PDF] [CODE]
Ensemble Distillation for Robust Model Fusion in Federated Learning	EPFL	NeurIPS	2020	FedDF²⁴⁰	[PUB] [PDF] [CODE]
Throughput-Optimal Topology Design for Cross-Silo Federated Learning	INRIA	NeurIPS	2020		[PUB] [PDF] [CODE]
Bayesian Nonparametric Federated Learning of Neural Networks 🔥	IBM	ICML	2019		[PUB] [PDF] [CODE]
Analyzing Federated Learning through an Adversarial Lens 🔥	Princeton; IBM	ICML	2019		[PUB] [PDF] [CODE]
Agnostic Federated Learning	Google	ICML	2019		[PUB] [PDF]
cpSGD: Communication-efficient and differentially-private distributed SGD	Princeton; Google	NeurIPS	2018		[PUB] [PDF]
Federated Multi-Task Learning 🔥	Stanford; USC; CMU	NeurIPS	2017		[PUB] [PDF] [CODE]

fl in top dm conference and journal

In this section, we will summarize Federated Learning papers accepted by top DM(Data Mining) conference and journal, Including KDD(ACM SIGKDD Conference on Knowledge Discovery and Data Mining) and WSDM(Web Search and Data Mining).

KDD 2022(Research Track, Applied Data Science track) , 2021,2020
WSDM 2023, 2022, 2021, 2019

Title	Affiliation	Venue	Year	TL;DR	Materials
Federated Unlearning for On-Device Recommendation	UQ	WSDM	2023		[PUB] [PDF]
Collaboration Equilibrium in Federated Learning	THU	KDD	2022	CE²⁴¹	[PUB] [PDF] [CODE]
Connected Low-Loss Subspace Learning for a Personalization in Federated Learning	Ulsan National Institute of Science and Technology	KDD	2022	SuPerFed²⁴²	[PUB] [PDF] [CODE]
FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks	University of Virginia	KDD	2022	FedMSplit²⁴³	[PUB]
Communication-Efficient Robust Federated Learning with Noisy Labels	University of Pittsburgh	KDD	2022	Comm-FedBiO²⁴⁴	[PUB] [PDF]
FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency	USTC	KDD	2022	FLDetector²⁴⁵	[PUB] [PDF] [CODE]
Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data	HKUST	KDD	2022	FedSVD²⁴⁶	[PUB] [PDF] [CODE]
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy	SJTU	KDD	2022	FedWalk⁶	[PUB] [PDF]
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning 🔥	Alibaba	KDD (Best Paper Award)	2022	FederatedScope-GNN⁷	[PUB] [PDF] [CODE]
Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch	BUAA	KDD	2022	Fed-LTD²⁴⁷	[PUB] [PDF] [解读]
Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks	USTC	KDD	2022	Felicitas²⁴⁸	[PUB] [PDF]
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices	Renmin University of China	KDD	2022	InclusiveFL²⁴⁹	[PUB] [PDF]
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling	THU	KDD	2022	FedAttack²⁵⁰	[PUB] [PDF] [CODE]
PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion	The University of Queensland	WSDM	2022	PipAttack²⁵¹	[PUB] [PDF]
Fed2: Feature-Aligned Federated Learning	George Mason University; Microsoft; University of Maryland	KDD	2021	Fed2²⁵²	[PUB] [PDF]
FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data	Nanjing University	KDD	2021	FedRS²⁵³	[PUB] [CODE]
Federated Adversarial Debiasing for Fair and Trasnferable Representations	Michigan State University	KDD	2021	FADE²⁵⁴	[PUB] [PAGE] [CODE] [SLIDE]
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling	USC	KDD	2021	CNFGNN⁴²	[PUB] [CODE] [解读]
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization	Xidian University;JD Tech	KDD	2021	AsySQN²⁵⁵	[PUB] [PDF]
FLOP: Federated Learning on Medical Datasets using Partial Networks	Duke University	KDD	2021	FLOP²⁵⁶	[PUB] [PDF] [CODE]
A Practical Federated Learning Framework for Small Number of Stakeholders	ETH Zürich	WSDM	2021	Federated-Learning-source²⁵⁷	[PUB] [CODE]
Federated Deep Knowledge Tracing	USTC	WSDM	2021	FDKT²⁵⁸	[PUB] [CODE]
FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems	University College Dublin	KDD	2020	FedFast²⁵⁹	[PUB] [VIDEO]
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data	JD Tech	KDD	2020	FDSKL²⁶⁰	[PUB] [PDF] [VIDEO]
Federated Online Learning to Rank with Evolution Strategies	Facebook AI Research	WSDM	2019	FOLtR-ES²⁶¹	[PUB] [CODE]

fl in top secure conference and journal

In this section, we will summarize Federated Learning papers accepted by top Secure conference and journal, Including S&P(IEEE Symposium on Security and Privacy), CCS(Conference on Computer and Communications Security), USENIX Security(Usenix Security Symposium) and NDSS(Network and Distributed System Security Symposium).

Title	Affiliation	Venue	Year	TL;DR	Materials
Securing Federated Sensitive Topic Classification against Poisoning Attacks	IMDEA Networks Institute	NDSS	2023		[PUB] [PDF] [CODE]
PPA: Preference Profiling Attack Against Federated Learning	NJUST	NDSS	2023		[PUB] [PDF]
CERBERUS: Exploring Federated Prediction of Security Events	UCL London	CCS	2022		[PUB] [PDF]
EIFFeL: Ensuring Integrity for Federated Learning	UW-Madison	CCS	2022		[PUB] [PDF]
Eluding Secure Aggregation in Federated Learning via Model Inconsistency	SPRING Lab; EPFL	CCS	2022		[PUB] [PDF] [CODE]
Federated Boosted Decision Trees with Differential Privacy	University of Warwick	CCS	2022		[PUB] [PDF] [CODE]
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information	Duke University	S&P	2023	FedRecover²⁶²	[PUB] [PDF]
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy	Fudan University	S&P	2023	PEA²⁶³	[PUB] [PDF]
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning	University of Massachusetts	S&P	2022		[PUB] [VIDEO]
SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost	Microsoft Research	USENIX Security	2022	SIMC²⁶⁴	[PUB] [PDF] [CODE] [VIDEO] [SUPP]
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors	University of Vermont	USENIX Security	2022		[PUB] [SLIDE] [VIDEO]
Label Inference Attacks Against Vertical Federated Learning	ZJU	USENIX Security	2022		[PUB] [SLIDE] [CODE] [VIDEO]
FLAME: Taming Backdoors in Federated Learning	Technical University of Darmstadt	USENIX Security	2022	FLAME²⁶⁵	[PUB] [SLIDE] [PDF] [VIDEO]
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning	University at Buffalo, SUNY	NDSS	2022		[PUB] [PDF] [VIDEO] [UC.]
Interpretable Federated Transformer Log Learning for Cloud Threat Forensics	University of the Incarnate Word	NDSS	2022		[PUB] [VIDEO] [UC.]
FedCRI: Federated Mobile Cyber-Risk Intelligence	Technical University of Darmstadt	NDSS	2022	FedCRI²⁶⁶	[PUB] [VIDEO]
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection	Technical University of Darmstadt	NDSS	2022	DeepSight²⁶⁷	[PUB] [PDF] [VIDEO]
Private Hierarchical Clustering in Federated Networks	NUS	CCS	2021		[PUB] [PDF]
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping	Duke University	NDSS	2021		[PUB] [PDF] [CODE] [VIDEO] [SLIDE]
POSEIDON: Privacy-Preserving Federated Neural Network Learning	EPFL	NDSS	2021		[PUB] [VIDEO]
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning	University of Massachusetts Amherst	NDSS	2021		[PUB] [CODE] [VIDEO]
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning	The Ohio State University	USENIX Security	2020		[PUB] [PDF] [CODE] [VIDEO] [SLIDE]
A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain	University of Kansas	CCS (Poster)	2019		[PUB]
IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning	Université du Québéc á Montréal	S&P （Workshop）	2019		[PUB]
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning 🔥	University of Massachusetts Amherst	S&P	2019		[PUB] [VIDEO] [SLIDE] [CODE]
Practical Secure Aggregation for Privacy Preserving Machine Learning	Google	CCS	2017		[PUB] [PDF] [解读] [UC.] [UC]

fl in top cv conference and journal

In this section, we will summarize Federated Learning papers accepted by top CV(computer vision) conference and journal, Including CVPR(Computer Vision and Pattern Recognition), ICCV(IEEE International Conference on Computer Vision), ECCV(European Conference on Computer Vision), MM(ACM International Conference on Multimedia), IJCV(International Journal of Computer Vision).

Title	Affiliation	Venue	Year	TL;DR	Materials
Rethinking Federated Learning With Domain Shift: A Prototype View	WHU	CVPR	2023		[PUB] [CODE]
Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning	ECNU	CVPR	2023		[PUB] [CODE]
DaFKD: Domain-Aware Federated Knowledge Distillation	HUST	CVPR	2023		[PUB] [CODE]
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning	Purdue University	CVPR	2023		[PUB] [PDF]
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation	ZJU	CVPR	2023		[PUB]
On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data	DTU	CVPR	2023		[PUB] [PDF]
Elastic Aggregation for Federated Optimization	Meituan	CVPR	2023		[PUB]
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning	UCLA	CVPR	2023		[PUB] [PDF]
Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity	UM	CVPR	2023		[PUB]
ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients	GaTech	CVPR	2023		[PUB] [CODE]
Reliable and Interpretable Personalized Federated Learning	TJU	CVPR	2023		[PUB]
Federated Domain Generalization With Generalization Adjustment	SJTU	CVPR	2023		[PUB] [CODE]
Make Landscape Flatter in Differentially Private Federated Learning	THU	CVPR	2023		[PUB] [PDF] [CODE]
Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization	KU Leuven	CVPR	2023		[PUB] [PDF] [CODE]
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection	GaTech	CVPR	2023		[PUB] [PDF] [CODE]
Re-Thinking Federated Active Learning Based on Inter-Class Diversity	KAIST	CVPR	2023		[PUB] [PDF] [CODE]
Learning Federated Visual Prompt in Null Space for MRI Reconstruction	A*STAR	CVPR	2023		[PUB] [PDF] [CODE]
Fair Federated Medical Image Segmentation via Client Contribution Estimation	CUHK	CVPR	2023		[PUB] [PDF] [CODE]
Federated Learning With Data-Agnostic Distribution Fusion	NJU	CVPR	2023		[PUB] [CODE]
How To Prevent the Poor Performance Clients for Personalized Federated Learning?	CSU	CVPR	2023		[PUB]
GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting	ECNU	CVPR	2023		[PUB] [PDF] [CODE]
Bias-Eliminating Augmentation Learning for Debiased Federated Learning	NTU	CVPR	2023		[PUB]
Federated Incremental Semantic Segmentation	CAS; UCAS	CVPR	2023		[PUB] [PDF] [CODE]
Confederated Learning: Going Beyond Centralization	CAS; UCAS	MM	2022		[PUB]
Few-Shot Model Agnostic Federated Learning	WHU	MM	2022	FSMAFL²⁶⁸	[PUB] [CODE]
Feeling Without Sharing: A Federated Video Emotion Recognition Framework Via Privacy-Agnostic Hybrid Aggregation	TJUT	MM	2022	EmoFed²⁶⁹	[PUB]
FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks		ECCV	2022		[PUB] [SUPP]
Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional Medical Image Segmentation		ECCV	2022		[PUB] [SUPP] [PDF] [CODE]
Improving Generalization in Federated Learning by Seeking Flat Minima	Politecnico di Torino	ECCV	2022	FedSAM²⁷⁰	[PUB] [SUPP] [PDF] [CODE]
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation		ECCV	2022		[PUB] [SUPP] [PDF] [CODE] [PAGE]
SphereFed: Hyperspherical Federated Learning		ECCV	2022		[PUB] [SUPP] [PDF]
Federated Self-Supervised Learning for Video Understanding		ECCV	2022		[PUB] [PDF] [CODE]
FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation		ECCV	2022		[PUB] [SUPP] [PDF] [CODE]
Addressing Heterogeneity in Federated Learning via Distributional Transformation		ECCV	2022		[PUB] [CODE]
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation	KAIST	ECCV	2022	FedX²⁷¹	[PUB] [SUPP] [PDF] [CODE]
Personalizing Federated Medical Image Segmentation via Local Calibration	Xiamen University	ECCV	2022	LC-Fed²⁷²	[PUB] [SUPP] [PDF] [CODE]
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework	HIT	CVPR	2022	ATPFL²⁷³	[PUB]
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning	Stanford	CVPR	2022	ViT-FL²⁷⁴	[PUB] [SUPP] [PDF] [CODE] [VIDEO]
FedCorr: Multi-Stage Federated Learning for Label Noise Correction	Singapore University of Technology and Design	CVPR	2022	FedCorr²⁷⁵	[PUB] [SUPP] [PDF] [CODE] [VIDEO]
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning	Duke University	CVPR	2022	FedCor²⁷⁶	[PUB] [SUPP] [PDF]
Layer-Wised Model Aggregation for Personalized Federated Learning	PolyU	CVPR	2022	pFedLA²⁷⁷	[PUB] [SUPP] [PDF]
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning	University of Central Florida	CVPR	2022	FedAlign²⁷⁸	[PUB] [SUPP] [PDF] [CODE]
Federated Learning With Position-Aware Neurons	Nanjing University	CVPR	2022	PANs²⁷⁹	[PUB] [SUPP] [PDF]
RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning	HKUST	CVPR	2022	RSCFed²⁸⁰	[PUB] [SUPP] [PDF] [CODE]
Learn From Others and Be Yourself in Heterogeneous Federated Learning	Wuhan University	CVPR	2022	FCCL²⁸¹	[PUB] [CODE] [VIDEO]
Robust Federated Learning With Noisy and Heterogeneous Clients	Wuhan University	CVPR	2022	RHFL²⁸²	[PUB] [SUPP] [CODE]
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning	Arizona State University	CVPR	2022	ResSFL²⁸³	[PUB] [SUPP] [PDF] [CODE]
FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction	National University of Defense Technology	CVPR	2022	FedDC²⁸⁴	[PUB] [PDF] [CODE] [解读]
Federated Class-Incremental Learning	CAS; Northwestern University; UTS	CVPR	2022	GLFC²⁸⁵	[PUB] [PDF] [CODE]
Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning	PKU; JD Explore Academy; The University of Sydney	CVPR	2022	FedFTG²⁸⁶	[PUB] [PDF]
Differentially Private Federated Learning With Local Regularization and Sparsification	CAS	CVPR	2022	DP-FedAvg+BLUR+LUS²⁸⁷	[PUB] [PDF]
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage	University of Tennessee; Oak Ridge National Laboratory; Google Research	CVPR	2022	GGL²⁸⁸	[PUB] [PDF] [CODE] [VIDEO]
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning	SJTU	CVPR	2022	CD2-pFed²⁸⁹	[PUB] [PDF]
Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation	Univ. of Pittsburgh; NVIDIA	CVPR	2022	FedSM²⁹⁰	[PUB] [PDF]
Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning	Johns Hopkins University	CVPR	2021	FL-MRCM²⁹¹	[PUB] [PDF] [CODE]
Model-Contrastive Federated Learning 🔥	NUS; UC Berkeley	CVPR	2021	MOON²⁹²	[PUB] [PDF] [CODE] [解读]
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space 🔥	CUHK	CVPR	2021	FedDG-ELCFS²⁹³	[PUB] [PDF] [CODE]
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective	Duke University	CVPR	2021	Soteria²⁹⁴	[PUB] [PDF] [CODE]
Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment	PKU	ICCV	2021	FedUFO²⁹⁵	[PUB]
Ensemble Attention Distillation for Privacy-Preserving Federated Learning	University at Buffalo	ICCV	2021	FedAD²⁹⁶	[PUB] [PDF]
Collaborative Unsupervised Visual Representation Learning from Decentralized Data	NTU; SenseTime	ICCV	2021	FedU²⁹⁷	[PUB] [PDF]
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification	NTU	MM	2021	FedUReID²⁹⁸	[PUB] [PDF]
Federated Visual Classification with Real-World Data Distribution	MIT; Google	ECCV	2020	FedVC+FedIR²⁹⁹	[PUB] [PDF] [VIDEO]
InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages		MM	2020	InvisibleFL³⁰⁰	[PUB]
Performance Optimization of Federated Person Re-identification via Benchmark Analysis `data.`	NTU	MM	2020	FedReID³⁰¹	[PUB] [PDF] [CODE] [解读]

fl in top nlp conference and journal

In this section, we will summarize Federated Learning papers accepted by top AI and NLP conference and journal, including ACL(Annual Meeting of the Association for Computational Linguistics), NAACL(North American Chapter of the Association for Computational Linguistics), EMNLP(Conference on Empirical Methods in Natural Language Processing) and COLING(International Conference on Computational Linguistics).

Title	Affiliation	Venue	Year	TL;DR	Materials
Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation	PKU	EMNLP	2022		[PUB] [PDF]
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation `kg.`	Lehigh University	EMNLP	2022	FedR²⁰	[PUB] [PDF] [CODE]
Federated Continual Learning for Text Classification via Selective Inter-client Transfer	DRIMCo GmbH; LMU	EMNLP	2022		[PUB] [PDF] [CODE]
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling	SNU	EMNLP	2022		[PUB] [PDF]
A Federated Approach to Predicting Emojis in Hindi Tweets	University of Alberta	EMNLP	2022		[PUB] [PDF] [CODE]
Federated Model Decomposition with Private Vocabulary for Text Classification	HIT; Peng Cheng Lab	EMNLP	2022		[PUB] [CODE]
Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification		EMNLP	2022		[PUB]
Fair NLP Models with Differentially Private Text Encoders		EMNLP	2022		[PUB] [PDF] [CODE]
Scaling Language Model Size in Cross-Device Federated Learning	Google	ACL workshop	2022	SLM-FL³⁰²	[PUB] [PDF]
Intrinsic Gradient Compression for Scalable and Efficient Federated Learning	Oxford	ACL workshop	2022	IGC-FL³⁰³	[PUB] [PDF]
ActPerFL: Active Personalized Federated Learning	Amazon	ACL workshop	2022	ActPerFL³⁰⁴	[PUB] [PAGE]
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks 🔥	USC	NAACL	2022	FedNLP³⁰⁵	[PUB] [PDF] [CODE]
Federated Learning with Noisy User Feedback	USC; Amazon	NAACL	2022	FedNoisy³⁰⁶	[PUB] [PDF]
Training Mixed-Domain Translation Models via Federated Learning	Amazon	NAACL	2022	FedMDT³⁰⁷	[PUB] [PAGE] [PDF]
Pretrained Models for Multilingual Federated Learning	Johns Hopkins University	NAACL	2022		[PUB] [PDF] [CODE]
Training Mixed-Domain Translation Models via Federated Learning	Amazon	NAACL	2022		[PUB] [PAGE] [PDF]
Federated Chinese Word Segmentation with Global Character Associations	University of Washington	ACL workshop	2021		[PUB] [CODE]
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation	USTC	EMNLP	2021	Efficient-FedRec³⁰⁸	[PUB] [PDF] [CODE] [VIDEO]
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories	CUHK (Shenzhen)	EMNLP	2021		[PUB] [CODE] [VIDEO]
A Secure and Efficient Federated Learning Framework for NLP	University of Connecticut	EMNLP	2021		[PUB] [PDF] [VIDEO]
Distantly Supervised Relation Extraction in Federated Settings	UCAS	EMNLP workshop	2021		[PUB] [PDF] [CODE]
Federated Learning with Noisy User Feedback	USC; Amazon	NAACL workshop	2021		[PUB] [PDF]
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework	Universität Hamburg	NAACL workshop	2021		[PUB]
Understanding Unintended Memorization in Language Models Under Federated Learning	Google	NAACL workshop	2021		[PUB] [PDF]
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction	CAS	EMNLP	2020		[PUB] [VIDEO] [解读]
Empirical Studies of Institutional Federated Learning For Natural Language Processing	Ping An Technology	EMNLP workshop	2020		[PUB]
Federated Learning for Spoken Language Understanding	PKU	COLING	2020		[PUB]
Two-stage Federated Phenotyping and Patient Representation Learning	Boston Children’s Hospital Harvard Medical School	ACL workshop	2019		[PUB] [PDF] [CODE] [UC.]

fl in top ir conference and journal

In this section, we will summarize Federated Learning papers accepted by top Information Retrieval conference and journal, including SIGIR(Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).

SIGIR 2022, 2021, 2020

Title	Affiliation	Venue	Year	TL;DR	Materials
Is Non-IID Data a Threat in Federated Online Learning to Rank?	The University of Queensland	SIGIR	2022	noniid-foltr³⁰⁹	[PUB] [CODE]
FedCT: Federated Collaborative Transfer for Recommendation	Rutgers University	SIGIR	2021	FedCT³¹⁰	[PUB] [PDF] [CODE]
On the Privacy of Federated Pipelines	Technical University of Munich	SIGIR	2021	FedGWAS³¹¹	[PUB]
FedCMR: Federated Cross-Modal Retrieval.	Dalian University of Technology	SIGIR	2021	FedCMR[^FedCMR]	[PUB] [CODE]
Meta Matrix Factorization for Federated Rating Predictions.	SDU	SIGIR	2020	MetaMF[^MetaMF]	[PUB] [PDF]

fl in top db conference and journal

In this section, we will summarize Federated Learning papers accepted by top Database conference and journal, including SIGMOD(ACM SIGMOD Conference) , ICDE(IEEE International Conference on Data Engineering) and VLDB(Very Large Data Bases Conference).

Title	Affiliation	Venue	Year	TL;DR	Materials
Differentially Private Vertical Federated Clustering.	Purdue University	VLDB	2023		[PUB] [PDF] [CODE]
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. 🔥	Alibaba	VLDB	2023		[PUB] [PDF] [CODE]
Secure Shapley Value for Cross-Silo Federated Learning.	Kyoto University	VLDB	2023		[PUB] [PDF] [CODE]
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization	ZJU	VLDB	2022	OpBoost⁸⁴	[PUB] [PDF] [CODE]
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy.	NUS	VLDB	2022	SMM[^SMM]	[PUB] [CODE]
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update	PKU	VLDB	2022	CELU-VFL[^CELU-VFL]	[PUB] [PDF] [CODE]
FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification.	HIT	VLDB	2022	FedTSC[^FedTSC]	[PUB] [CODE]
Improving Fairness for Data Valuation in Horizontal Federated Learning	The UBC	ICDE	2022	CSFV[^CSFV]	[PUB] [PDF]
FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity	USTC	ICDE	2022	FedADMM[^FedADMM]	[PUB] [PDF] [CODE]
FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing.	USTC	ICDE	2022	FedMP[^FedMP]	[PUB]
Federated Learning on Non-IID Data Silos: An Experimental Study. 🔥	NUS	ICDE	2022	ESND[^ESND]	[PUB] [PDF] [CODE]
Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing	USTC	ICDE	2022	FedMigr[^FedMigr]	[PUB]
Samba: A System for Secure Federated Multi-Armed Bandits	Univ. Clermont Auvergne	ICDE	2022	Samba[^Samba]	[PUB] [CODE]
FedRecAttack: Model Poisoning Attack to Federated Recommendation	ZJU	ICDE	2022	FedRecAttack[^FedRecAttack]	[PUB] [PDF] [CODE]
Enhancing Federated Learning with In-Cloud Unlabeled Data	USTC	ICDE	2022	Ada-FedSemi[^Ada-FedSemi]	[PUB]
Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning	USTC	ICDE	2022	DIG-FL[^DIG-FL]	[PUB]
An Introduction to Federated Computation	University of Warwick; Facebook	SIGMOD Tutorial	2022	FCT[^FCT]	[PUB]
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data	PKU; Tencent	SIGMOD	2022	BlindFL[^BlindFL]	[PUB] [PDF]
An Efficient Approach for Cross-Silo Federated Learning to Rank	BUAA	ICDE	2021	CS-F-LTR[^CS-F-LTR]	[PUB] [RELATED PAPER(ZH)]
Feature Inference Attack on Model Predictions in Vertical Federated Learning	NUS	ICDE	2021	FIA[^FIA]	[PUB] [PDF] [CODE]
Efficient Federated-Learning Model Debugging	USTC	ICDE	2021	FLDebugger[^FLDebugger]	[PUB]
Federated Matrix Factorization with Privacy Guarantee	Purdue	VLDB	2021	FMFPG[^FMFPG]	[PUB]
Projected Federated Averaging with Heterogeneous Differential Privacy.	Renmin University of China	VLDB	2021	PFA-DB[^PFA-DB]	[PUB] [CODE]
Enabling SQL-based Training Data Debugging for Federated Learning	Simon Fraser University	VLDB	2021	FedRain-and-Frog[^FedRain-and-Frog]	[PUB] [PDF] [CODE]
Refiner: A Reliable Incentive-Driven Federated Learning System Powered by Blockchain	ZJU	VLDB	2021	Refiner[^Refiner]	[PUB]
Tanium Reveal: A Federated Search Engine for Querying Unstructured File Data on Large Enterprise Networks	Tanium Inc.	VLDB	2021	TaniumReveal[^TaniumReveal]	[PUB] [VIDEO]
VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning	PKU	SIGMOD	2021	VF2Boost⁹⁷	[PUB]
ExDRa: Exploratory Data Science on Federated Raw Data	SIEMENS	SIGMOD	2021	ExDRa[^ExDRa]	[PUB]
Joint blockchain and federated learning-based offloading in harsh edge computing environments	TJU	SIGMOD workshop	2021	FLoffloading[^FLoffloading]	[PUB]
Privacy Preserving Vertical Federated Learning for Tree-based Models	NUS	VLDB	2020	Pivot-DT¹⁰⁷	[PUB] [PDF] [VIDEO] [CODE]

fl in top network conference and journal

In this section, we will summarize Federated Learning papers accepted by top Database conference and journal, including SIGCOMM(Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication), INFOCOM(IEEE Conference on Computer Communications), MobiCom(ACM/IEEE International Conference on Mobile Computing and Networking), NSDI(Symposium on Networked Systems Design and Implementation) and WWW(The Web Conference).

SIGCOMM NULL
INFOCOM 2023, 2022(Page), 2021(Page), 2020(Page), 2019, 2018
MobiCom 2022, 2021, 2020
NSDI 2023(Spring, Fall)
WWW 2023, 2022, 2021

Title	Affiliation	Venue	Year	TL;DR	Materials
FLASH: Towards a High-performance Hardware Acceleration Architecture for Cross-silo Federated Learning	HKUST; Clustar	NSDI	2023		[PUB] [SLIDE] [VIDEO]
To Store or Not? Online Data Selection for Federated Learning with Limited Storage.	SJTU	WWW	2023		[PUB] [PDF]
pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning.	PolyU	WWW	2023		[PUB]
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding.	ZJU; HIC-ZJU	WWW	2023		[PUB] [PDF]
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks	PKU	WWW	2023		[PUB] [PDF] [CODE]
Semi-decentralized Federated Ego Graph Learning for Recommendation	SUST	WWW	2023		[PUB] [PDF]
FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures.	Swinburne	WWW	2023		[PUB] [CODE]
FedEdge: Accelerating Edge-Assisted Federated Learning.	Swinburne	WWW	2023		[PUB]
Federated Node Classification over Graphs with Latent Link-type Heterogeneity.	Emory University	WWW	2023		[PUB] [CODE]
FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection.	USTC	WWW	2023		[PUB] [PDF] [CODE]
Interaction-level Membership Inference Attack Against Federated Recommender Systems.	UQ	WWW	2023		[PUB] [PDF]
AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated Learning.	Deakin University	WWW	2023		[PUB]
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning.	NJU	WWW	2023		[PUB] [PDF] [CODE]
Federated Learning for Metaverse: A Survey.	JNU	WWW (Companion Volume)	2023		[PUB] [PDF]
Understanding the Impact of Label Skewness and Optimization on Federated Learning for Text Classification	KU Leuven	WWW (Companion Volume)	2023		[PUB]
Privacy-Preserving Online Content Moderation: A Federated Learning Use Case.	CUT	WWW (Companion Volume)	2023		[PUB] [PDF]
Privacy-Preserving Online Content Moderation with Federated Learning.	CUT	WWW (Companion Volume)	2023		[PUB]
A Federated Learning Benchmark for Drug-Target Interaction.	University of Turin	WWW (Companion Volume)	2023		[PUB] [PDF] [CODE]
Towards a Decentralized Data Hub and Query System for Federated Dynamic Data Spaces.	TU Berlin	WWW (Companion Volume)	2023		[PUB]
1st Workshop on Federated Learning Technologies1st Workshop on Federated Learning Technologies	University of Turin	WWW (Companion Volume)	2023		[PUB]
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy	CUHK	WWW (Companion Volume)	2023		[PUB] [PDF]
A Hierarchical Knowledge Transfer Framework for Heterogeneous Federated Learning	THU	INFOCOM	2023
A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning	Southeast University	INFOCOM	2023
Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning	USTC	INFOCOM	2023	FedHP[^FedHP]	[PDF]
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices	Guangdong University of Technology	INFOCOM	2023	AnycostFL[^AnycostFL]	[PDF]
AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation	HUST	INFOCOM	2023	AOCC-FL[^AOCC-FL]
Asynchronous Federated Unlearning	University of Toronto	INFOCOM	2023	KNOT[^KNOT]	[PDF]
Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization	PSU	INFOCOM	2023		[PDF]
Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing	Beihang University	INFOCOM	2023
Federated Learning under Heterogeneous and Correlated Client Availability	Inria	INFOCOM	2023	CA-Fed[^CA-Fed]	[PDF] [CODE]
Federated Learning with Flexible Control	IBM	INFOCOM	2023	FlexFL[^FlexFL]	[PDF]
Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks	The University of Sydney	INFOCOM	2023		[PDF]
FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection	HUST	INFOCOM	2023	FedMoS[^FedMoS]	[PDF]
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning	NTU	INFOCOM	2023	FedSDG-FS[^FedSDG-FS]
Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression	USTC	INFOCOM	2023
Joint Edge Aggregation and Association for Cost-Efficient Multi-Cell Federated Learning	NUDT	INFOCOM	2023
Joint Participation Incentive and Network Pricing Design for Federated Learning	Northwestern University	INFOCOM	2023
More than Enough is Too Much: Adaptive Defenses against Gradient Leakage in Production Federated Learning	University of Toronto	INFOCOM	2023	OUTPOST[^OUTPOST]	[PDF]
Network Adaptive Federated Learning: Congestion and Lossy Compression	UTAustin	INFOCOM	2023	NAC-FL[^NAC-FL]	[PDF]
OBLIVION: Poisoning Federated Learning by Inducing Catastrophic Forgetting	The Hang Seng University of Hong Kong	INFOCOM	2023	OBLIVION[^OBLIVION]
Privacy as a Resource in Differentially Private Federated Learning	BUPT	INFOCOM	2023
SplitGP: Achieving Both Generalization and Personalization in Federated Learning	KAIST	INFOCOM	2023	SplitGP[^SplitGP]	[PDF]
SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition	Beihang University	INFOCOM	2023	SVDFed[^SVDFed]
Tackling System Induced Bias in Federated Learning: Stratification and Convergence Analysis	Southern University of Science and Technology	INFOCOM	2023		[PDF]
Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions	BUPT	INFOCOM	2023		[PDF]
Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling	Auburn University	INFOCOM	2023		[PDF]
TVFL: Tunable Vertical Federated Learning towards Communication-Efficient Model Serving	USTC	INFOCOM	2023	TVFL[^TVFL]
PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning	MSU	MobiCom	2022	PyramidFL[^PyramidFL]	[PUB] [PDF] [CODE]
NestFL: efficient federated learning through progressive model pruning in heterogeneous edge computing	pmlabs	MobiCom(Poster)	2022		[PUB]
Federated learning-based air quality prediction for smart cities using BGRU model	IITM	MobiCom(Poster)	2022		[PUB]
FedHD: federated learning with hyperdimensional computing	UCSD	MobiCom(Demo)	2022		[PUB] [CODE]
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks	Korea University	INFOCOM	2022	SlimFL[^SlimFL]	[PUB]
Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending	University of Toronto	INFOCOM	2022	HGBFL[^HGBFL]	[PUB]
Optimal Rate Adaption in Federated Learning with Compressed Communications	SZU	INFOCOM	2022	ORAFL[^ORAFL]	[PUB] [PDF]
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining.	CityU	INFOCOM	2022	RFFL[^RFFL]	[PUB] [PDF]
Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling.	CUHK; AIRS ;Yale University	INFOCOM	2022	FLACS[^FLACS]	[PUB] [PDF]
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization	Army Research Laboratory, Adelphi	INFOCOM	2022	CEDSFL[^CEDSFL]	[PUB] [PDF]
FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors	NEU	INFOCOM	2022	FLASH[^FLASH]	[PUB] [CODE]
A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning	CUHK; AIRS	INFOCOM	2022	PMDPFL[^PMDPFL]	[PUB]
Protect Privacy from Gradient Leakage Attack in Federated Learning	PolyU	INFOCOM	2022	PPGLFL[^PPGLFL]	[PUB] [SLIDE]
FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent Pattern Mining.	SJTU	INFOCOM	2022	FedFPM[^FedFPM]	[PUB] [CODE]
An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning	SWJTU;THU	WWW	2022	PBPFL[^PBPFL]	[PUB] [PDF] [CODE]
LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning	Yonsei University	WWW	2022	LocFedMix-SL[^LocFedMix-SL]	[PUB]
Federated Unlearning via Class-Discriminative Pruning	PolyU	WWW	2022		[PUB] [PDF] [CODE]
FedKC: Federated Knowledge Composition for Multilingual Natural Language Understanding	Purdue	WWW	2022	FedKC[^FedKC]	[PUB]
Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing.		WWW (Companion Volume)	2022
Federated Bandit: A Gossiping Approach	University of California	SIGMETRICS	2021	Federated-Bandit[^Federated-Bandit]	[PUB] [PDF]
Hermes: an efficient federated learning framework for heterogeneous mobile clients	Duke University	MobiCom	2021	Hermes[^Hermes]	[PUB]
Federated mobile sensing for activity recognition	Samsung AI Center	MobiCom	2021		[PUB] [PAGE] [TALKS] [VIDEO]
Learning for Learning: Predictive Online Control of Federated Learning with Edge Provisioning.	Nanjing University	INFOCOM	2021		[PUB]
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation.	Purdue	INFOCOM	2021	D2D-FedL³⁹	[PUB] [PDF]
FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation	THU	INFOCOM	2021	FAIR[^FAIR]	[PUB]
Sample-level Data Selection for Federated Learning	USTC	INFOCOM	2021		[PUB]
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices	Xidian University; CAS	INFOCOM	2021		[PUB] [PDF]
Cost-Effective Federated Learning Design	CUHK; AIRS; Yale University	INFOCOM	2021		[PUB] [PDF]
An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective	The UBC	INFOCOM	2021		[PUB]
Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing	USTC	INFOCOM	2021		[PUB]
FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism.	Jinan University; CityU	INFOCOM	2021	FedServing[^FedServing]	[PUB] [PDF]
Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach	Arizona State University	INFOCOM	2021		[PUB] [PDF]
Dual Attention-Based Federated Learning for Wireless Traffic Prediction	King Abdullah University of Science and Technology	INFOCOM	2021	FedDA[^FedDA]	[PUB] [PDF] [CODE]
FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing	University of Notre Dame	INFOCOM	2021	FedSens[^FedSens]	[PUB]
P-FedAvg: Parallelizing Federated Learning with Theoretical Guarantees	SYSU; Guangdong Key Laboratory of Big Data Analysis and Processing	INFOCOM	2021	P-FedAvg[^P-FedAvg]	[PUB]
Meta-HAR: Federated Representation Learning for Human Activity Recognition.	University of Alberta	WWW	2021	Meta-HAR[^Meta-HAR]	[PUB] [PDF] [CODE]
PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization	PKU	WWW	2021	PFA[^PFA]	[PUB] [PDF] [CODE]
Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics	Emory	WWW	2021	FedGTF-EF-PC[^FedGTF-EF-PC]	[PUB] [CODE]
Hierarchical Personalized Federated Learning for User Modeling	USTC	WWW	2021		[PUB]
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data	PKU	WWW	2021	Heter-aware[^Heter-aware]	[PUB] [PDF] [SLIDE] [CODE]
Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction	SYSU	WWW	2021		[PUB]
Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled Wireless Networks.	Nanjing University	INFOCOM	2020		[PUB]
Optimizing Federated Learning on Non-IID Data with Reinforcement Learning 🔥	University of Toronto	INFOCOM	2020		[PUB] [SLIDE] [CODE] [解读]
Enabling Execution Assurance of Federated Learning at Untrusted Participants	THU	INFOCOM	2020		[PUB] [CODE]
Billion-scale federated learning on mobile clients: a submodel design with tunable privacy	SJTU	MobiCom	2020		[PUB]
Federated Learning over Wireless Networks: Optimization Model Design and Analysis	The University of Sydney	INFOCOM	2019		[PUB] [CODE]
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning	Wuhan University	INFOCOM	2019		[PUB] [PDF] [UC.]
InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy	Collaborative Innovation Center of Geospatial Technology	INFOCOM	2018	TFL[^TFL]	[PUB]

fl in top system conference and journal

In this section, we will summarize Federated Learning papers accepted by top Database conference and journal, including OSDI(USENIX Symposium on Operating Systems Design and Implementation), SOSP(Symposium on Operating Systems Principles), ISCA(International Symposium on Computer Architecture), MLSys(Conference on Machine Learning and Systems), TPDS(IEEE Transactions on Parallel and Distributed Systems), DAC(Design Automation Conference), TOCS(ACM Transactions on Computer Systems), TOS(ACM Transactions on Storage), TCAD(IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems), TC(IEEE Transactions on Computers).

OSDI 2021
SOSP 2021
ISCA NULL
MLSys 2022, 2020, 2019
TPDS 2023, 2022, 2021, 2020
DAC 2022, 2021
TOCS NULL
TOS NULL
TCAD 2023, 2022, 2021
TC 2023, 2022, 2021

Title	Affiliation	Venue	Year	TL;DR	Materials
Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing	ECNU	TCAD	2023		[PUB]
Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms	TU Dortmund University	TC	2023		[PUB] [CODE]
Sandbox Computing: A Data Privacy Trusted Sharing Paradigm Via Blockchain and Federated Learning.	BUPT	TC	2023		[PUB]
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-Based Federated Learning.	IUPUI	TPDS	2023		[PUB] [PDF]
HiFlash: Communication-Efficient Hierarchical Federated Learning With Adaptive Staleness Control and Heterogeneity-Aware Client-Edge Association.		TPDS	2023		[PUB] [PDF]
From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization.		TPDS	2023		[PUB] [PDF] [CODE]
Federated Learning Over Coupled Graphs	XJTU	TPDS	2023		[PUB] [PDF]
Privacy vs. Efficiency: Achieving Both Through Adaptive Hierarchical Federated Learning	NUDT	TPDS	2023		[PUB]
On Model Transmission Strategies in Federated Learning With Lossy Communications	SZU	TPDS	2023		[PUB]
Scheduling Algorithms for Federated Learning With Minimal Energy Consumption	University of Bordeaux	TPDS	2023		[PUB] [PDF] [CODE]
Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems	HIT	TPDS	2023		[PUB] [PDF]
Personalized Edge Intelligence via Federated Self-Knowledge Distillation.	HUST	TPDS	2023		[PUB] [CODE]
Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning.	HIT	TPDS	2023		[PUB]
Multi-Job Intelligent Scheduling With Cross-Device Federated Learning.	Baidu	TPDS	2023		[PUB] [PDF]
Data-Centric Client Selection for Federated Learning Over Distributed Edge Networks.	IIT	TPDS	2023		[PUB]
GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication.	HKBU	TPDS	2023		[PUB]
FedMDS: An Efficient Model Discrepancy-Aware Semi-Asynchronous Clustered Federated Learning Framework.	CQU	TPDS	2023		[PUB]
HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing.	DUT	TPDS	2023		[PUB]
BAFL: A Blockchain-Based Asynchronous Federated Learning Framework		TC	2022		[PUB] [CODE]
L4L: Experience-Driven Computational Resource Control in Federated Learning		TC	2022		[PUB]
Adaptive Federated Learning on Non-IID Data With Resource Constraint		TC	2022		[PUB]
Locking Protocols for Parallel Real-Time Tasks With Semaphores Under Federated Scheduling.		TCAD	2022		[PUB]
Client Scheduling and Resource Management for Efficient Training in Heterogeneous IoT-Edge Federated Learning	ECNU	TCAD	2022		[PUB]
PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT Systems.	ECNU	TCAD	2022	PervasiveFL[^PervasiveFL]	[PUB]
FHDnn: communication efficient and robust federated learning for AIoT networks	UC San Diego	DAC	2022	FHDnn[^FHDnn]	[PUB]
A Decentralized Federated Learning Framework via Committee Mechanism With Convergence Guarantee	SYSU	TPDS	2022		[PUB] [PDF]
Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation	THU	TPDS	2022		[PUB]
$f$funcX: Federated Function as a Service for Science.	SUST	TPDS	2022		[PUB] [PDF]
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation	NUST	TPDS	2022		[PUB] [PDF] [CODE]
Adaptive Federated Deep Reinforcement Learning for Proactive Content Caching in Edge Computing.	CQU	TPDS	2022		[PUB]
TDFL: Truth Discovery Based Byzantine Robust Federated Learning	BIT	TPDS	2022		[PUB]
Federated Learning With Nesterov Accelerated Gradient	The University of Sydney	TPDS	2022		[PUB] [PDF]
FedGraph: Federated Graph Learning with Intelligent Sampling	UoA	TPDS	2022	FedGraph¹³	[PUB] [CODE] [解读]
AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning.	THU	TPDS	2022	AUCTION[^AUCTION]	[PUB]
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning.	University of Sydney	TPDS	2022	DONE[^DONE]	[PUB] [PDF] [CODE]
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift.	CQU	TPDS	2022	FlexCFL[^FlexCFL]	[PUB] [PDF] [CODE]
Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks.	Xidian University	TPDS	2022		[PUB]
LightFed: An Efficient and Secure Federated Edge Learning System on Model Splitting.	CSU	TPDS	2022	LightFed[^LightFed]	[PUB]
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning.	Purdue	TPDS	2022	Deli-CoCo[^Deli-CoCo]	[PUB] [PDF] [CODE]
Incentive-Aware Autonomous Client Participation in Federated Learning.	Sun Yat-sen University	TPDS	2022		[PUB]
Communicational and Computational Efficient Federated Domain Adaptation.	HKUST	TPDS	2022		[PUB]
Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning.	NTU	TPDS	2022		[PUB]
Differentially Private Byzantine-Robust Federated Learning.	Qufu Normal University	TPDS	2022	DPBFL[^DPBFL]	[PUB]
Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing.	University of Exeter	TPDS	2022		[PUB] [PDF] [CODE]
Reputation-Aware Hedonic Coalition Formation for Efficient Serverless Hierarchical Federated Learning.	BUAA	TPDS	2022	SHFL[^SHFL]	[PUB]
Differentially Private Federated Temporal Difference Learning.	Stony Brook University	TPDS	2022		[PUB]
Towards Efficient and Stable K-Asynchronous Federated Learning With Unbounded Stale Gradients on Non-IID Data.	XJTU	TPDS	2022	WKAFL[^WKAFL]	[PUB] [PDF]
Communication-Efficient Federated Learning With Compensated Overlap-FedAvg.	SCU	TPDS	2022	Overlap-FedAvg[^Overlap-FedAvg]	[PUB] [PDF] [CODE]
PAPAYA: Practical, Private, and Scalable Federated Learning.	Meta AI	MLSys	2022	PAPAYA[^PAPAYA]	[PDF] [PUB]
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning	USC	MLSys	2022	LightSecAgg[^LightSecAgg]	[PDF] [PUB] [CODE]
SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead	University of Warwick	TC	2021	SAFA[^SAFA]	[PDF] [PUB] [CODE]
Efficient Federated Learning for Cloud-Based AIoT Applications	ECNU	TCAD	2021		[PUB]
HADFL: Heterogeneity-aware Decentralized Federated Learning Framework	USTC	DAC	2021	HADFL[^HADFL]	[PDF] [PUB]
Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration.	GMU	DAC	2021	Helios[^Helios]	[PDF] [PUB]
FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control.	ECNU	DAC	2021	FedLight[^FedLight]	[PUB]
Oort: Efficient Federated Learning via Guided Participant Selection	University of Michigan	OSDI	2021	Oort[^Oort]	[PUB] [PDF] [CODE] [SLIDES] [VIDEO]
Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity.	Old Dominion University	TPDS	2021		[PUB] [PDF]
Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems.	CQU	TPDS	2021	Astraea[^Astraea]	[PUB] [CODE]
An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee	SCUT	TPDS	2021	RBCS-F[^RBCS-F]	[PUB] [PDF] [解读]
Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm.	Beijing Normal University	TPDS	2021	PoFL[^PoFL]	[PUB] [PDF]
Biscotti: A Blockchain System for Private and Secure Federated Learning.	UBC	TPDS	2021	Biscotti[^Biscotti]	[PUB]
Mutual Information Driven Federated Learning.	Deakin University	TPDS	2021		[PUB]
Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile Edge Computing Systems.	University of Warwick	TPDS	2021		[PUB] [PDF]
FedSCR: Structure-Based Communication Reduction for Federated Learning.	HKU	TPDS	2021	FedSCR[^FedSCR]	[PUB]
FedScale: Benchmarking Model and System Performance of Federated Learning 🔥	University of Michigan	SOSP workshop / ICML 2022	2021	FedScale¹⁸⁷	[PUB] [PDF] [CODE] [解读]
Redundancy in cost functions for Byzantine fault-tolerant federated learning		SOSP workshop	2021		[PUB]
Towards an Efficient System for Differentially-private, Cross-device Federated Learning		SOSP workshop	2021		[PUB]
GradSec: a TEE-based Scheme Against Federated Learning Inference Attacks		SOSP workshop	2021		[PUB]
Community-Structured Decentralized Learning for Resilient EI.		SOSP workshop	2021		[PUB]
Separation of Powers in Federated Learning (Poster Paper)	IBM Research	SOSP workshop	2021	TRUDA[^TRUDA]	[PUB] [PDF]
Accelerating Federated Learning via Momentum Gradient Descent.	USTC	TPDS	2020	MFL[^MFL]	[PUB] [PDF]
Towards Fair and Privacy-Preserving Federated Deep Models.	NUS	TPDS	2020	FPPDL[^FPPDL]	[PUB] [PDF] [CODE]
Federated Optimization in Heterogeneous Networks 🔥	CMU	MLSys	2020	FedProx[^FedProx]	[PUB] [PDF] [CODE]
Towards Federated Learning at Scale: System Design	Google	MLSys	2019	System_Design[^System_Design]	[PUB] [PDF] [解读]

fl in top conference and journal other fields

In this section, we will summarize Federated Learning papers accepted by top conference and journal in the other fields, including ICSE(International Conference on Software Engineering).

ICSE 2023

Title	Affiliation	Venue	Year	TL;DR	Materials

framework

federated learning framework

table

Note: SG means Support for Graph data and algorithms, ST means Support for Tabular data and algorithms.

Platform	Papers	Affiliations	SG	ST	Materials
PySyft	A generic framework for privacy preserving deep learning	OpenMined			[DOC]
FATE	FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection	WeBank		✅✅	[DOC] [DOC(ZH)]
MindSpore Federated		HUAWEI			[DOC] [PAGE]
FedML	FedML: A Research Library and Benchmark for Federated Machine Learning	FedML	✅✅	✅	[DOC]
TFF(Tensorflow-Federated)	Towards Federated Learning at Scale: System Design	Google			[DOC] [PAGE]
Flower	Flower: A Friendly Federated Learning Research Framework	flower.dev adap			[DOC]
SecretFlow		Ant group		✅	[DOC]
FederatedScope	FederatedScope: A Flexible Federated Learning Platform for Heterogeneity	Alibaba DAMO Academy	✅✅		[DOC] [PAGE]
Fedlearner		Bytedance
LEAF	LEAF: A Benchmark for Federated Settings	CMU
Rosetta		matrixelements			[DOC] [PAGE]
OpenFL	OpenFL: An open-source framework for Federated Learning	Intel			[DOC]
PFL-Non-IID		SJTU
Fedlab	FedLab: A Flexible Federated Learning Framework	SMILELab			[DOC] [DOC(ZH)] [PAGE]
PaddleFL		Baidu			[DOC]
IBM Federated Learning	IBM Federated Learning: an Enterprise Framework White Paper	IBM		✅	[PAPERS]
Privacy Meter	Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning	University of Massachusetts Amherst
Primihub		primihub			[DOC]
KubeFATE		WeBank			[WIKI]
NVFlare		NVIDIA			[DOC]
NIID-Bench	Federated Learning on Non-IID Data Silos: An Experimental Study	Xtra Computing Group
Differentially Private Federated Learning: A Client-level Perspective	Differentially Private Federated Learning: A Client Level Perspective	SAP-samples
FedScale	FedScale: Benchmarking Model and System Performance of Federated Learning at Scale	SymbioticLab(U-M)
easyFL	Federated Learning with Fair Averaging	XMU
Backdoors 101	Blind Backdoors in Deep Learning Models	Cornell Tech
SWARM LEARNING	Swarm Learning for decentralized and confidential clinical machine learning				[VIDEO]
substra		Substra			[DOC]
FedJAX	FEDJAX: Federated learning simulation with JAX	Google
plato		UofT
FedNLP	FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks	FedML
Galaxy Federated Learning	GFL: A Decentralized Federated Learning Framework Based On Blockchain	ZJU			[DOC]
Xaynet		XayNet			[PAGE] [DOC] [WHITEPAPER] [LEGAL REVIEW]
SyferText		OpenMined
EasyFL	EasyFL: A Low-code Federated Learning Platform For Dummies	NTU
FLSim		facebook research
Breaching	A Framework for Attacks against Privacy in Federated Learning (papers)
FedGraphNN	FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks	FedML	✅✅
PyVertical	PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN	OpenMined
FLUTE	FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations	microsoft			[DOC]
FedTorch	Distributionally Robust Federated Averaging	Penn State
FLSim	Optimizing Federated Learning on Non-IID Data with Reinforcement Learning	University of Toronto
PhotoLabeller					[BLOG]
FATE-Serving		WeBank			[DOC]
PriMIA	End-to-end privacy preserving deep learning on multi-institutional medical imaging	TUM; Imperial College London; OpenMined			[DOC]
9nfl		JD
FedTree		Xtra Computing Group		✅✅	[DOC]
FedLearn	Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform	JD
FEDn	Scalable federated machine learning with FEDn	scaleoutsystems			[DOC]
FedCV	FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks	FedML
FeTS	The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research	Federated Tumor Segmentation (FeTS) initiative			[DOC]
MPLC		LabeliaLabs			[PAGE]
UCADI	Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence	Huazhong University of Science and Technology
Flame		Cisco			[DOC]
APPFL					[DOC]
FlexCFL	Flexible Clustered Federated Learning for Client-Level Data Distribution Shift	Chongqing University
OpenFed	OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework				[DOC]
FedGroup	FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure	Chongqing University
FedEval	FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning	HKU			[DOC]
FedSim
Federated-Learning-source	A Practical Federated Learning Framework for Small Number of Stakeholders	ETH Zürich			[DOC]
Clara		NVIDIA
OpenHealth		ZJU

benchmark

UniFed leaderboard

Here's a really great Benchmark for the federated learning open source framework 👍 UniFed leaderboard, which present both qualitative and quantitative evaluation results of existing popular open-sourced FL frameworks, from the perspectives of functionality, usability, and system performance.

For more results, please refer to Framework Functionality Support

datasets

graph datasets

tabular datasets

fl datasets

surveys

This section partially refers to repository Federated-Learning and FederatedAI research , the order of the surveys is arranged in reverse order according to the time of first submission (the latest being placed at the top)

[SIGKDD Explor. 2022] Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications PUB PDF
[ACM Trans. Interact. Intell. Syst.] Toward Responsible AI: An Overview of Federated Learning for User-centered Privacy-preserving Computing [PUB]
[ICML Workshop 2020] SECure: A Social and Environmental Certificate for AI Systems PDF
[IEEE Commun. Mag. 2020] From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks PDF [PUB]
[China Communications 2020] Federated Learning for 6G Communications: Challenges, Methods, and Future Directions PDF [PUB.]
[Federated Learning Systems] A Review of Privacy Preserving Federated Learning for Private IoT Analytics PDF [PUB]
[WorldS4 2020] Survey of Personalization Techniques for Federated Learning PDF [PUB]
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective PDF
[IEEE Internet Things J. 2022] A Survey on Federated Learning for Resource-Constrained IoT Devices PDF [PUB]
[IEEE Communications Surveys & Tutorials 2020] Communication-Efficient Edge AI: Algorithms and Systems PDF [PUB]
[IEEE Communications Surveys & Tutorials 2020] Federated Learning in Mobile Edge Networks: A Comprehensive Survey PDF [PUB]
[IEEE Signal Process. Mag. 2020] Federated Learning: Challenges, Methods, and Future Directions PDF [PUB]
[IEEE Commun. Mag. 2020] Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges PDF [PUB]
[IEEE TKDE 2021] A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection PDF [PUB]
[IJCAI Workshop 2020] Threats to Federated Learning: A Survey PDF
[Foundations and Trends in Machine Learning 2021] Advances and Open Problems in Federated Learning PDF [PUB]
Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing PDF
An Introduction to Communication Efficient Edge Machine Learning PDF
[IEEE Communications Surveys & Tutorials 2020] Convergence of Edge Computing and Deep Learning: A Comprehensive Survey PDF [PUB]
[IEEE TIST 2019] Federated Machine Learning: Concept and Applications PDF [PUB]
[J. Heal. Informatics Res. 2021] Federated Learning for Healthcare Informatics PDF [PUB]
Federated Learning for Coalition Operations PDF
No Peek: A Survey of private distributed deep learning PDF

tutorials and courses

tutorials

联邦学习入门教程参考
[NeurIPS 2020] Federated Learning Tutorial [Web] [Slides] [Video]
Federated Learning on MNIST using a CNN, AI6101, 2020 (Demo Video)
[AAAI 2019] Federated Learning: User Privacy, Data Security and Confidentiality in Machine Learning
Applied Cryptography
A Brief Introduction to Differential Privacy
Deep Learning with Differential Privacy.
Building Safe A.I.
- A Tutorial for Encrypted Deep Learning
- Use Homomorphic Encryption (HE)
Private Image Analysis with MPC
- Training CNNs on Sensitive Data
- Use SPDZ as MPC protocol
Private Deep Learning with MPC
- A Simple Tutorial from Scratch
- Use Multiparty Compuation (MPC)

course

secret sharing

Simple Introduction to Sharmir's Secret Sharing and Lagrange Interpolation
Secret Sharing
- Part 1: Shamir's Secret Sharing & Packed Variant
- Part 2: Improve efficiency
- Part 3: Robust Reconstruction

key conferences/workshops/journals

This section partially refers to The Federated Learning Portal.

workshops

[FL-IJCAI'23], International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2023 (FL-IJCAI'23), Macau
[FL-KDD'23], International Workshop on Federated Learning for Distributed Data Mining Co-located with the 29th ACM SIGKDD Conference (KDD 2023), Long Beach, CA, USA
[FL-ICML'23],Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities Workshop at ICML 2023, Honolulu, HI, USA
[FLIRT-SIGIR'23],1st Workshop on Federated Learning for Information ReTrieval, Taipei, Taiwan
[FLSys'23], the Federated Learning Systems (FLSys) Workshop @ MLSys 2023, Miami, FL, USA
[FLW@TheWebConf'23], 1st Workshop on Federated Learning Technologies, Austin, TX, USA
[CIKM'22] The 1st International Workshop on Federated Learning with Graph Data (FedGraph), Atlanta, GA, USA
[AI Technology School 2022] Trustable, Verifiable and Auditable Artificial Intelligence, Singapore
[FL-NeurIPS'22] International Workshop on Federated Learning: Recent Advances and New Challenges in Conjunction with NeurIPS 2022 , New Orleans, LA, USA
[FL-IJCAI'22] International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022, Vienna, Austria
[FL-AAAI-22] International Workshop on Trustable, Verifiable and Auditable Federated Learning in Conjunction with AAAI 2022, Vancouver, BC, Canada (Virtual)
[FL-MobiCom'22] FedEdge 2022, 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network -Research Track, Sydney, Australia
[FL-NeurIPS'21] New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership, (Virtual)
[The Federated Learning Workshop, 2021] , Paris, France (Hybrid)
[PDFL-EMNLP'21] Workshop on Parallel, Distributed, and Federated Learning, Bilbao, Spain (Virtual)
[FTL-IJCAI'21] International Workshop on Federated and Transfer Learning for Data Sparsity and Confidentiality in Conjunction with IJCAI 2021, Montreal, QB, Canada (Virtual)
[DeepIPR-IJCAI'21] Toward Intellectual Property Protection on Deep Learning as a Services, Montreal, QB, Canada (Virtual)
[FL-ICML'21] International Workshop on Federated Learning for User Privacy and Data Confidentiality, (Virtual)
[RSEML-AAAI-21] Towards Robust, Secure and Efficient Machine Learning, (Virtual)
[NeurIPS-SpicyFL'20] Workshop on Scalability, Privacy, and Security in Federated Learning, Vancouver, BC, Canada (Virtual)
[FL-IJCAI'20] International Workshop on Federated Learning for User Privacy and Data Confidentiality, Yokohama, Japan (Virtual)
[FL-ICML'20] International Workshop on Federated Learning for User Privacy and Data Confidentiality, Vienna, Austria (Virtual)
[FL-IBM'20] Workshop on Federated Learning and Analytics, New York, NY, USA
[FL-NeurIPS'19] Workshop on Federated Learning for Data Privacy and Confidentiality (in Conjunction with NeurIPS 2019), Vancouver, BC, Canada
[FL-IJCAI'19] International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2019, Macau
[FL-Google'19] Workshop on Federated Learning and Analytics, Seattle, WA, USA

journal special issues

Special Issue on Trustable, Verifiable, and Auditable Federated Learning, IEEE Transactions on Big Data (TBD), 2022.
Special Issue on Federated Learning: Algorithms, Systems, and Applications, ACM Transactions on Intelligent Systems and Technology (TIST), 2021.
Special Issue on Federated Machine Learning, IEEE Intelligent Systems (IS), 2019.

conference special tracks

"Federated Learning" included as a new keyword in IJCAI'20, Yokohama, Japan
Special Track on Federated Machine Learning, IEEE BigData'19, Los Angeles, CA, USA

update log

2023/05/23 - add CVPR 2023 papers
2023/05/07 - add workshops and WWW 2023 papers
2023/04/02 - add NDSS 2023 papers and fix some typos
2023/02/19 - add INFOCOM 2023 papers
2023/02/14 - add EMNLP 2022 papers
2023/02/13 - add ICLR 2023 papers
2023/01/14 - add UAI 2022 papers, refresh system (TCAD +1, TPDS+8), ML (TPAMI +1,UAI +6), network(MobiCom +3) fields papers
2022/11/24 - refresh NeurIPS 2022,2021 and ICLR 2022 papers
2022/11/06- add S&P 2023 papers
2022/10/29 - add WSDM 2023 paper
2022/10/20 - add CCS, MM, ECCV 2022 papers
2022/10/16 - add AI, JMLR, TPAMI, IJCV, TOCS, TOS, TCAD, TC papers
2022/10/13 - add DAC papers
2022/10/09 - add MobiCom 2022 paper
2022/09/19 - add NeurIPS 2022 papers
2022/09/16 - repository is online with Github Pages
2022/09/06 - add information about FL on Tabular and Graph data
2022/09/05 - add some information about top journals and add TPDS papers
2022/08/31 - all papers (including 400+ papers from top conferences and top journals and 100+ papers with graph and tabular data) have been comprehensively sorted out, and information such as publication addresses, links to preprints and source codes of these papers have been compiled. The source code of 280+ papers has been obtained. We hope it can help those who use this project. 😃
2022/07/31 - add VLDB papers
2022/07/30 - add top-tier system conferences papers and add COLT,UAI,OSDI, SOSP, ISCA, MLSys, AISTATS,WSDM papers
2022/07/28 - add a list of top-tier conferences papers and add IJCAI,SIGIR,SIGMOD,ICDE,WWW,SIGCOMM.INFOCOM,WWW papers
2022/07/27 - add some ECCV 2022 papers
2022/07/22 - add CVPR 2022 and MM 2020,2021 papers
2022/07/21 - give TL;DR and interpret information(解读) of papers. And add KDD 2022 papers
2022/07/15 - give a list of papers in the field of federated learning in top NLP/Secure conferences. And add ICML 2022 papers
2022/07/14 - give a list of papers in the field of federated learning in top ML/CV/AI/DM conferences from innovation-cat‘s Awesome-Federated-Machine-Learning and find 🔥 papers(code is available & stars >= 100)
2022/07/12 - added information about the last commit time of the federated learning open source framework (can be used to determine the maintenance of the code base)
2022/07/12 - give a list of papers in the field of federated learning in top journals
2022/05/25 - complete the paper and code lists of FL on tabular data and Tree algorithms
2022/05/25 - add the paper list of FL on tabular data and Tree algorithms
2022/05/24 - complete the paper and code lists of FL on graph data and Graph Neural Networks
2022/05/23 - add the paper list of FL on graph data and Graph Neural Networks
2022/05/21 - update all of Federated Learning Framework

how to contact us

More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ (im.young@foxmail.com). Enjoy reading!

acknowledgments

Many thanks ❤️ to the other awesome list:

citation

@misc{awesomeflGTD,
    title = {Awesome-Federated-Learning-on-Graph-and-Tabular-Data},
    author = {Yuwen Yang, Bingjie Yan, Xuefeng Jiang, Hongcheng Li, Jian Wang, Jiao Chen, Xiangmou Qu, Chang Liu and others},
    year = {2022},
    howpublished = {\\url{https://github.com/youngfish42/Awesome-Federated-Learning-on-Graph-and-Tabular-Data}
}

TBC ↩
A visual analytics tool, HetVis, for participating clients to explore data heterogeneity. We identify data heterogeneity through comparing prediction behaviors of the global federated model and the stand-alone model trained with local data. Then, a context-aware clustering of the inconsistent records is done, to provide a summary of data heterogeneity. Combining with the proposed comparison techniques, we develop a novel set of visualizations to identify heterogeneity issues in HFL(Horizontal federated learning). 可视化分析工具Het Vis，用于参与客户探索数据异质性。我们通过比较全局联邦模型和使用本地数据训练的单机模型的预测行为来识别数据异构性。然后，对不一致记录进行上下文感知的聚类，以提供数据异质性的总结。结合所提出的比较技术，我们开发了一套新颖的可视化来识别HFL(横向联邦学习)中的异质性问题。 ↩
From real-world graph datasets, we observe that some structural properties are shared by various domains, presenting great potential for sharing structural knowledge in FGL. Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks. To explicitly extract the structure information rather than encoding them along with the node features, we define structure embeddings and encode them with an independent structure encoder. Then, the structure encoder is shared across clients while the feature-based knowledge is learned in a personalized way, making FedStar capable of capturing more structure-based domain-invariant information and avoiding feature misalignment issues. We perform extensive experiments over both cross-dataset and cross-domain non-IID FGL settings. 从现实世界的图数据集中，我们观察到一些结构属性被不同的领域所共享，这为联邦图机器学习**享结构知识提供了巨大的潜力。受此启发，我们提出了FedStar，一个为图间联合学习任务提取和分享共同基础结构信息的FGL框架。为了明确地提取结构信息，而不是将其与节点特征一起编码，我们定义了结构嵌入，并用一个独立的结构编码器对其进行编码。然后，结构编码器在客户之间共享，而基于特征的知识则以个性化的方式学习，这使得FedStar能够捕获更多基于结构的领域变量信息，并避免了特征错位问题。我们在跨数据集和跨域的非IID FGL设置上进行了广泛的实验。 ↩ ↩²
Federated Graph-based Sampling (FedGS) to stabilize the global model update and mitigate the long-term bias given arbitrary client availability simultaneously. First, we model the data correlations of clients with a Data-Distribution-Dependency Graph (3DG) that helps keep the sampled clients data apart from each other, which is theoretically shown to improve the approximation to the optimal model update. Second, constrained by the far-distance in data distribution of the sampled clients, we further minimize the variance of the numbers of times that the clients are sampled, to mitigate long-term bias. 基于图的联合采样（Federated Graph-based Sampling，FedGS）稳定了全局模型的更新，并同时减轻了任意客户端可用性的长期偏差。首先，我们用数据分布-依赖图（3DG）对客户的数据相关性进行建模，这有助于使被采样的客户数据相互分离，理论上证明这可以提高对最佳模型更新的近似度。其次，受制于被抽样客户数据分布的远距离，我们进一步将客户被抽样次数的方差降到最低，以减轻长期偏差。 ↩ ↩²
TBC ↩
FedWalk, a random-walk-based unsupervised node embedding algorithm that operates in such a node-level visibility graph with raw graph information remaining locally. FedWalk，一个基于随机行走的无监督节点嵌入算法，在这样一个节点级可见度图中操作，原始图信息保留在本地。 ↩ ↩²
FederatedScope-GNN present an easy-to-use FGL (federated graph learning) package. FederatedScope-GNN提出了一个易于使用的FGL（联邦图学习）软件包。 ↩ ↩²
GAMF formulate the model fusion problem as a graph matching task, considering the second-order similarity of model weights instead of previous work merely formulating model fusion as a linear assignment problem. For the rising problem scale and multi-model consistency issues, GAMF propose an efficient graduated assignment-based model fusion method, iteratively updates the matchings in a consistency-maintaining manner. GAMF将模型融合问题表述为图形匹配任务，考虑了模型权重的二阶相似性，而不是之前的工作仅仅将模型融合表述为一个线性赋值问题。针对问题规模的扩大和多模型的一致性问题，GAMF提出了一种高效的基于分级赋值的模型融合方法，以保持一致性的方式迭代更新匹配结果。 ↩
We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG needs to embed an emerging KG with unseen entities and relations. To solve this problem, we introduce the meta-learning setting, where a set of tasks are sampled on the existing KG to mimic the link prediction task on the emerging KG. Based on sampled tasks, we meta-train a graph neural network framework that can construct features for unseen components based on structural information and output embeddings for them. 我们研究了知识外推问题，以嵌入新的组件（即实体和关系），这些组件来自于联邦设置的新兴知识图（KGs）。在这个问题上，一个在现有KG上训练的模型需要嵌入一个带有未见过的实体和关系的新兴KG。为了解决这个问题，我们引入了元学习设置，在这个设置中，一组任务在现有的KG上被抽样，以模拟新兴KG上的链接预测任务。基于抽样任务，我们对图神经网络框架进行元训练，该框架可以根据结构信息为未见过的组件构建特征，并为其输出嵌入。 ↩ ↩²
A novel structured federated learning (SFL) framework to enhance the knowledge-sharing process in PFL by leveraging the graph-based structural information among clients and learn both the global and personalized models simultaneously using client-wise relation graphs and clients' private data. We cast SFL with graph into a novel optimization problem that can model the client-wise complex relations and graph-based structural topology by a unified framework. Moreover, in addition to using an existing relation graph, SFL could be expanded to learn the hidden relations among clients. 一个新的结构化联邦学习（SFL）框架通过利用客户之间基于图的结构信息来加强PFL中的知识共享过程，并使用客户的关系图和客户的私人数据同时学习全局和个性化的模型。我们把带图的SFL变成一个新的优化问题，它可以通过一个统一的框架对客户的复杂关系和基于图的结构拓扑进行建模。此外，除了使用现有的关系图之外，SFL还可以扩展到学习客户之间的隐藏关系。 ↩ ↩²
VFGNN, a federated GNN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GNN models. Specifically, we split the computation graph into two parts. We leave the private data (i.e., features, edges, and labels) related computations on data holders, and delegate the rest of computations to a semi-honest server. We also propose to apply differential privacy to prevent potential information leakage from the server. VFGNN是一种联邦的GNN学习范式，适用于数据纵向分割情况下的隐私保护节点分类任务，它可以被推广到现有的GNN模型。具体来说，我们将计算图分成两部分。我们将私有数据（即特征、边和标签）相关的计算留给数据持有者，并将其余的计算委托给半诚实的服务器。我们还提议应用差分隐私来防止服务器的潜在信息泄露。 ↩ ↩²
SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature. We provide convergence guarantees and empirically demonstrate the efficacy of our framework on a variety of non-I.I.D. distributed graph-level molecular property prediction datasets with partial labels. SpreadGNN首次提出一个新颖的多任务联邦训练框架，能够在存在部分标签和没有**服务器的情况下运行。我们提供了收敛保证，并在各种具有部分标签的非I.I.D.分布式图级分子特性预测数据集上实证了我们框架的功效。我们的研究结果表明，SpreadGNN优于通过依赖**服务器的联邦学习系统训练的GNN模型，即使在受限的拓扑结构中也是如此。 ↩ ↩²
FedGraph for federated graph learning among multiple computing clients, each of which holds a subgraph. FedGraph provides strong graph learning capability across clients by addressing two unique challenges. First, traditional GCN training needs feature data sharing among clients, leading to risk of privacy leakage. FedGraph solves this issue using a novel cross-client convolution operation. The second challenge is high GCN training overhead incurred by large graph size. We propose an intelligent graph sampling algorithm based on deep reinforcement learning, which can automatically converge to the optimal sampling policies that balance training speed and accuracy. FedGraph 用于多个计算客户端之间的联邦图学习，每个客户端都有一个子图。FedGraph通过解决两个独特的挑战，跨客户端提供了强大的图形学习能力。首先，传统的GCN训练需要在客户之间进行功能数据共享，从而导致隐私泄露的风险。FedGraph使用一种新的跨客户端卷积操作来解决了这个问题。第二个挑战是大图所产生的高GCN训练开销。提出了一种基于深度强化学习的智能图采样算法，该算法可以自动收敛到最优的平衡训练速度和精度的采样策略。 ↩ ↩²
FGML a comprehensive review of the literature in Federated Graph Machine Learning. FGML 对图联邦机器学习的文献进行了全面回顾的综述文章。 ↩
TBC ↩
FedNI, to leverage network inpainting and inter-institutional data via FL. Specifically, we first federatively train missing node and edge predictor using a graph generative adversarial network (GAN) to complete the missing information of local networks. Then we train a global GCN node classifier across institutions using a federated graph learning platform. The novel design enables us to build more accurate machine learning models by leveraging federated learning and also graph learning approaches. FedNI，通过 FL 来利用网络补全和机构间数据。具体来说，我们首先使用图生成对抗网络（GAN）对缺失节点和边缘预测器进行联邦训练，以完成局部网络的缺失信息。然后，我们使用联邦图学习平台跨机构训练全局 GCN 节点分类器。新颖的设计使我们能够通过利用联邦学习和图学习方法来构建更准确的机器学习模型。 ↩
This work focuses on the graph classification task with partially labeled data. (1) Enhancing the collaboration processes: We propose a new personalized FL framework to deal with Non-IID data. Clients with more similar data have greater mutual influence, where the similarities can be evaluated via unlabeled data. (2) Enhancing the local training process: We introduce auxiliary loss for unlabeled data that restrict the training process. We propose a new pseudo-label strategy for our SemiGraphFL framework to make more effective predictions. 这项工作专注于具有部分标记数据的图分类任务。(1) 加强合作过程。我们提出了一个新的个性化的FL框架来处理非IID数据。拥有更多相似数据的客户有更大的相互影响，其中的相似性可以通过未标记的数据进行评估。(2) 加强本地训练过程。我们为未标记的数据引入了辅助损失，限制了训练过程。我们为我们的SemiGraphFL框架提出了一个新的伪标签策略，以做出更有效的预测。 ↩
FedPerGNN, a federated GNN framework for both effective and privacy-preserving personalization. Through a privacy-preserving model update method, we can collaboratively train GNN models based on decentralized graphs inferred from local data. To further exploit graph information beyond local interactions, we introduce a privacy-preserving graph expansion protocol to incorporate high-order information under privacy protection. FedPerGNN是一个既有效又保护隐私的GNN联盟框架。通过一个保护隐私的模型更新方法，我们可以根据从本地数据推断出的分散图来协作训练GNN模型。为了进一步利用本地互动以外的图信息，我们引入了一个保护隐私的图扩展协议，在保护隐私的前提下纳入高阶信息。 ↩ ↩²
A graph neural network model based on federated learning named GraphSniffer to identify malicious transactions in the digital currency market. GraphSniffer leverages federated learning and graph neural networks to model graph-structured Bitcoin transaction data distributed at different worker nodes, and transmits the gradients of the local model to the server node for aggregation to update the parameters of the global model. GraphSniffer 一种基于联邦学习的图神经网络模型来识别数字货币市场中的恶意交易。GraphSniffer 利用联邦学习和图神经网络对分布在不同工作节点的图结构比特币交易数据进行建模，并将局部模型的梯度传递到服务器节点进行聚合，更新全局模型的参数。 ↩
In this paper, we first develop a novel attack that aims to recover the original data based on embedding information, which is further used to evaluate the vulnerabilities of FedE. Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FedR) to tackle the privacy issue in FedE. Compared to entity embedding sharing, relation embedding sharing policy can significantly reduce the communication cost due to its smaller size of queries. 在本文中，我们首先开发了一个新颖的攻击，旨在基于嵌入信息恢复原始数据，并进一步用于评估FedE的漏洞。此外，我们提出了一种带有隐私保护的关系嵌入聚合（FedR）的联邦学习范式，以解决FedE的隐私问题。与实体嵌入共享相比，关系嵌入共享策略由于其较小的查询规模，可以大大降低通信成本。 ↩ ↩²
A data-driven approach for power allocation in the context of federated learning (FL) over interference-limited wireless networks. The power policy is designed to maximize the transmitted information during the FL process under communication constraints, with the ultimate objective of improving the accuracy and efficiency of the global FL model being trained. The proposed power allocation policy is parameterized using a graph convolutional network and the associated constrained optimization problem is solved through a primal-dual algorithm. 在干扰有限的无线网络上联邦学习（FL）的背景下，一种数据驱动的功率分配方法。功率策略的设计是为了在通信约束下的联邦学习过程中最大化传输信息，其最终目的是提高正在训练的全局联邦学习模型的准确性和效率。所提出的功率分配策略使用图卷积网络进行参数化，相关的约束性优化问题通过原始-双重算法进行解决。 ↩
We investigate multi-task learning (MTL), where multiple learning tasks are performed jointly rather than separately to leverage their similarities and improve performance. We focus on the federated multi-task linear regression setting, where each machine possesses its own data for individual tasks and sharing the full local data between machines is prohibited. Motivated by graph regularization, we propose a novel fusion framework that only requires a one-shot communication of local estimates. Our method linearly combines the local estimates to produce an improved estimate for each task, and we show that the ideal mixing weight for fusion is a function of task similarity and task difficulty. 我们研究了多任务学习（MTL），其中多个学习任务被关联而不是单独执行，以利用它们的相似性并提高性能。我们专注于联邦多任务线性回归的设置，其中每台机器拥有自己的个别任务的数据，并且禁止在机器之间共享完整的本地数据。在图正则化的启发下，我们提出了一个新的融合框架，只需要一次本地估计的交流。我们的方法线性地结合本地估计，为每个任务产生一个改进的估计，我们表明，融合的理想混合权重是任务相似性和任务难度的函数。 ↩
FedEC framework, a local training procedure is responsible for learning knowledge graph embeddings on each client based on a specific embedding learner. We apply embedding-contrastive learning to limit the embedding update for tackling data heterogeneity. Moreover, a global update procedure is used for sharing and averaging entity embeddings on the master server. 在FedEC框架中，一个本地训练程序负责在每个客户端上基于特定的嵌入学习者学习知识图的嵌入。我们应用嵌入对比学习来限制嵌入的更新，以解决数据的异质性问题。此外，全局更新程序被用于共享和平均主服务器上的实体嵌入。 ↩
Existing FL paradigms are inefficient for geo-distributed GCN training since neighbour sampling across geo-locations will soon dominate the whole training process and consume large WAN bandwidth. We derive a practical federated graph learning algorithm, carefully striking the trade-off among GCN convergence error, wall-clock runtime, and neighbour sampling interval. Our analysis is divided into two cases according to the budget for neighbour sampling. In the unconstrained case, we obtain the optimal neighbour sampling interval, that achieves the best trade-off between convergence and runtime; in the constrained case, we show that determining the optimal sampling interval is actually an online problem and we propose a novel online algorithm with bounded competitive ratio to solve it. Combining the two cases, we propose a unified algorithm to decide the neighbour sampling interval in federated graph learning, and demonstrate its effectiveness with extensive simulation over graph datasets. 现有的FL范式对于地理分布式的GCN训练是低效的，因为跨地理位置的近邻采样很快将主导整个训练过程，并消耗大量的广域网带宽。我们推导了一个实用的联邦图学习算法，仔细权衡了GCN收敛误差、wall - clock运行时间和近邻采样间隔。我们的分析根据邻居抽样的预算分为两种情况。在无约束的情况下，我们得到了最优的近邻采样间隔，实现了收敛性和运行时间的最佳折衷；在有约束的情况下，我们证明了确定最优采样间隔实际上是一个在线问题，并提出了一个新的有界竞争比的在线算法来解决这个问题。结合这两种情况，我们提出了一个统一的算法来决定联邦图学习中的近邻采样间隔，并通过在图数据集上的大量仿真证明了其有效性 ↩
Social bot detection is essential for the social network's security. Existing methods almost ignore the differences in bot behaviors in multiple domains. Thus, we first propose a DomainAware detection method with Multi-Relational Graph neural networks (DA-MRG) to improve detection performance. Specifically, DA-MRG constructs multi-relational graphs with users' features and relationships, obtains the user presentations with graph embedding and distinguishes bots from humans with domainaware classifiers. Meanwhile, considering the similarity between bot behaviors in different social networks, we believe that sharing data among them could boost detection performance. However, the data privacy of users needs to be strictly protected. To overcome the problem, we implement a study of federated learning framework for DA-MRG to achieve data sharing between different social networks and protect data privacy simultaneously. 社交机器人检测对于社交网络的安全至关重要。现有方法几乎忽略了多个域中机器人行为的差异。因此，本文首先提出一种基于多关系图神经网络(DA-MRG)的Domain Aware检测方法，以提高检测性能。具体来说，DA-MRG利用用户的特征和关系构建多关系图，通过图嵌入获得用户表示，并通过领域感知分类器区分机器人和人类。同时，考虑到不同社交网络中机器人行为之间的相似性，我们认为在它们之间共享数据可以提高检测性能。然而，用户的数据隐私需要严格保护。为了克服这个问题，我们实现了一个面向DA-MRG的联邦学习框架研究，以实现不同社交网络之间的数据共享，同时保护数据隐私。 ↩
The DP-based federated GNN has not been well investigated, especially in the sub-graph-level setting, such as the scenario of recommendation system. DP-FedRec, a DP-based federated GNN to fill the gap. Private Set Intersection (PSI) is leveraged to extend the local graph for each client, and thus solve the non-IID problem. Most importantly, DP(differential privacy) is applied not only on the weights but also on the edges of the intersection graph from PSI to fully protect the privacy of clients. 基于DP的联邦GNN还没有得到很好的研究，特别是在子图层面的设置，如推荐系统的场景。DP-FedRec，一个基于DP的联盟式GNN来填补这一空白。隐私集合求交（PSI）被用来扩展每个客户端的本地图，从而解决非IID问题。最重要的是，DP（差分隐私）不仅适用于权重，也适用于PSI中交集图的边，以充分保护客户的隐私。 ↩
TBC ↩
C lustering-based hierarchical and T wo-step- optimized FL (CTFL) employs a divide-and-conquer strategy, clustering clients based on the closeness of their local model parameters. Furthermore, we incorporate the particle swarm optimization algorithm in CTFL, which employs a two-step strategy for optimizing local models. This technique enables the central server to upload only one representative local model update from each cluster, thus reducing the communication overhead associated with model update transmission in the FL. 基于聚类的层次化和两步优化的FL ( CTFL )采用分治策略，根据本地模型参数的接近程度对客户端进行聚类。此外，我们将粒子群优化算法集成到CTFL中，该算法采用两步策略优化局部模型。此技术使中心服务器能够仅从每个集群上载一个有代表性的本地模型更新，从而减少与FL中模型更新传输相关的通信开销。 ↩
A privacy-preserving spatial-temporal prediction technique via federated learning (FL). Due to inherent non-independent identically distributed (non-IID) characteristic of spatial-temporal data, the basic FL-based method cannot deal with this data heterogeneity well by sharing global model; furthermore, we propose the personalized federated learning methods based on meta-learning. We automatically construct the global spatial-temporal pattern graph under a data federation. This global pattern graph incorporates and memorizes the local learned patterns of all of the clients, and each client leverages those global patterns to customize its own model by evaluating the difference between global and local pattern graph. Then, each client could use this customized parameters as its model initialization parameters for spatial-temporal prediction tasks. 一种通过联邦学习(FL)保护隐私的时空预测技术。由于时空数据固有的非独立同分布(non-IID)特性，基本的基于FL的方法无法通过共享全局模型很好地处理这种数据异构性；此外，我们提出了基于元学习的个性化联邦学习方法。我们在数据联邦下自动构建全局时空模式图。这个全局模式图包含并记忆了所有客户机的本地学习模式，每个客户机利用这些全局模式通过评估全局模式图和本地模式图之间的差异来定制自己的模型。然后，每个客户端可以使用这个定制的参数作为其时空预测任务的模型初始化参数。 ↩
We investigate FL scenarios in which data owners are related by a network topology (e.g., traffic prediction based on sensor networks). Existing personalized FL approaches cannot take this information into account. To address this limitation, we propose the Bilevel Optimization enhanced Graph-aided Federated Learning (BiG-Fed) approach. The inner weights enable local tasks to evolve towards personalization, and the outer shared weights on the server side target the non-i.i.d problem enabling individual tasks to evolve towards a global constraint space. To the best of our knowledge, BiG-Fed is the first bilevel optimization technique to enable FL approaches to cope with two nested optimization tasks at the FL server and FL clients simultaneously. 我们研究了数据所有者与网络拓扑相关的 FL 场景（例如，基于传感器网络的流量预测）。现有的个性化 FL 方法无法将这些信息考虑在内。为了解决这个限制，我们提出了双层优化增强的图形辅助联邦学习（BiG-Fed）方法。内部权重使本地任务向个性化发展，而服务器端的外部共享权重针对非独立同分布问题，使单个任务向全局约束空间发展。据我们所知，BiG-Fed 是第一个使 FL 方法能够同时处理 FL 服务器和 FL 客户端的两个嵌套优化任务的双层优化技术。 ↩
We explore the threat of collusion attacks from multiple malicious clients who pose targeted attacks (e.g., label flipping) in a federated learning configuration. By leveraging client weights and the correlation among them, we develop a graph-based algorithm to detect malicious clients. 我们探讨了来自多个恶意客户的串通攻击的威胁，这些客户在联邦学习配置中提出了有针对性的攻击（例如，标签翻转）。通过利用客户端的权重和它们之间的关联性，我们开发了一种基于图的算法来检测恶意客户端。 ↩ ↩²
Federated learning allows end users to build a global model collaboratively while keeping their training data isolated. We ﬁrst simulate a heterogeneous federated-learning benchmark (FedChem) by jointly performing scaffold splitting and latent Dirichlet allocation on existing datasets. Our results on FedChem show that signiﬁcant learning challenges arise when working with heterogeneous molecules across clients. We then propose a method to alleviate the problem: Federated Learning by Instance reweighTing (FLIT+). FLIT+ can align local training across clients. Experiments conducted on FedChem validate the advantages of this method. 联邦学习允许最终用户协同构建全局模型，同时保持他们的训练数据是孤立的。我们首先通过在现有数据集上联邦执行支架拆分和隐狄利克雷分配来模拟一个异构的联邦学习基准FedChem 。我们在FedChem上的研究结果表明，在跨客户端处理异构分子时，会出现显著的学习挑战。然后，我们提出了一种缓解该问题的方法：实例重加权联邦学习FLIT + 。FLIT+可以跨客户对齐本地训练。在FedChem上进行的实验验证了这种方法的优势。 ↩
Deep learning-based Wi-Fi indoor fingerprint localization, which requires a large received signal strength (RSS) dataset for training. A multi-level federated graph learning and self-attention based personalized indoor localization method is proposed to further capture the intrinsic features of RSS(received signal strength), and learn the aggregation manner of shared information uploaded by clients, with better personalization accuracy. 基于深度学习的Wi-Fi室内指纹定位，需要一个大的接收信号强度( RSS )数据集进行训练。为了进一步捕获RSS(接收信号强度)的内在特征，学习客户端上传的共享信息的聚合方式，具有更好的个性化精度，提出了一种基于多级联邦图学习和自注意力机制的个性化室内定位方法。 ↩
This paper proposes a decentralized online multitask learning algorithm based on GFL (O-GFML). Clients update their local models using continuous streaming data while clients and multiple servers can train different but related models simul-taneously. Furthermore, to enhance the communication efficiency of O-GFML, we develop a partial-sharing-based O-GFML (PSO-GFML). The PSO-GFML allows participating clients to exchange only a portion of model parameters with their respective servers during a global iteration, while non-participating clients update their local models if they have access to new data. 本文提出了一种基于GFL (O-GFML)的去中心化在线多任务学习算法。客户端使用连续的流数据更新本地模型，而客户端和多个服务器可以同时训练不同但相关的模型。此外，为了提高O-GFML的通信效率，我们开发了一种基于部分共享的O-GFML (PSO-GFML)。PSO-GFML允许参与的客户端在全局迭代过程中只与各自的服务器交换部分模型参数，而非参与的客户端在有机会获得新数据的情况下更新本地模型。 ↩
TBC ↩
AI healthcare applications rely on sensitive electronic healthcare records (EHRs) that are scarcely labelled and are often distributed across a network of the symbiont institutions. In this work, we propose dynamic neural graphs based federated learning framework to address these challenges. The proposed framework extends Reptile , a model agnostic meta-learning (MAML) algorithm, to a federated setting. However, unlike the existing MAML algorithms, this paper proposes a dynamic variant of neural graph learning (NGL) to incorporate unlabelled examples in the supervised training setup. Dynamic NGL computes a meta-learning update by performing supervised learning on a labelled training example while performing metric learning on its labelled or unlabelled neighbourhood. This neighbourhood of a labelled example is established dynamically using local graphs built over the batches of training examples. Each local graph is constructed by comparing the similarity between embedding generated by the current state of the model. The introduction of metric learning on the neighbourhood makes this framework semi-supervised in nature. The experimental results on the publicly available MIMIC-III dataset highlight the effectiveness of the proposed framework for both single and multi-task settings under data decentralisation constraints and limited supervision. 人工智能医疗应用依赖于敏感的电子医疗记录( EHR )，这些记录几乎没有标签，而且往往分布在共生体机构的网络中。在这项工作中，我们提出了基于动态神经图的联邦学习框架来解决这些挑战。提出的框架将模型不可知元学习(MAML)算法Reptile扩展到联邦环境。然而，与现有的MAML算法不同，本文提出了神经图学习(Neural Graph Learning，NGL 的动态变体，以在有监督的训练设置中纳入未标记的示例。动态NGL通过对带标签的训练示例执行监督学习，同时对其带标签或未带标签的邻域执行度量学习来计算元学习更新。标记样本的这个邻域是使用在批量训练样本上建立的局部图动态建立的。通过比较由模型的当前状态生成的嵌入之间的相似性来构造每个局部图。在邻域上引入度量学习使得这个框架具有半监督的性质。 ↩
A Federated Learning-Based Graph Convolutional Network (FedGCN). First, we propose a Graph Convolutional Network (GCN) as a local model of FL. Based on the classical graph convolutional neural network, TopK pooling layers and full connection layers are added to this model to improve the feature extraction ability. Furthermore, to prevent pooling layers from losing information, cross-layer fusion is used in the GCN, giving FL an excellent ability to process non-Euclidean spatial data. Second, in this paper, a federated aggregation algorithm based on an online adjustable attention mechanism is proposed. The trainable parameter ρ is introduced into the attention mechanism. The aggregation method assigns the corresponding attention coefficient to each local model, which reduces the damage caused by the inefficient local model parameters to the global model and improves the fault tolerance and accuracy of the FL algorithm. 基于联邦学习的图卷积网络(Fedgcn)。首先，我们提出了一个图卷积网络(GCN)作为FL的局部模型。该模型在经典图卷积神经网络的基础上，增加了Top K池化层和全连接层，提高了特征提取能力。此外，为了防止池化层丢失信息，在GCN中使用跨层融合，使FL具有处理非欧几里得空间数据的出色能力。其次，本文提出了一种基于在线可调注意力机制的联邦聚合算法。可训练参数ρ被引入注意力机制。聚合方法为每个局部模型分配相应的注意力系数，减少了低效的局部模型参数对全局模型造成的破坏，提高了FL算法的容错性和准确性。 ↩
Distributed surveillance systems have the ability to detect, track, and snapshot objects moving around in a certain space. The systems generate video data from multiple personal devices or street cameras. Intelligent video-analysis models are needed to learn dynamic representation of the objects for detection and tracking. In this work, we introduce Federated Dynamic Graph Neural Network (Feddy), a distributed and secured framework to learn the object representations from graph sequences: (1) It aggregates structural information from nearby objects in the current graph as well as dynamic information from those in the previous graph. It uses a self-supervised loss of predicting the trajectories of objects. (2) It is trained in a federated learning manner. The centrally located server sends the model to user devices. Local models on the respective user devices learn and periodically send their learning to the central server without ever exposing the user’s data to server. (3) Studies showed that the aggregated parameters could be inspected though decrypted when broadcast to clients for model synchronizing, after the server performed a weighted average. 分布式监控系统有能力检测、跟踪和抓拍在一定空间内移动的物体。这些系统从多个个人设备或街道摄像机产生视频数据。需要智能视频分析模型来学习物体的动态表示，以便进行检测和跟踪。在这项工作中，我们引入了联邦动态图谱神经网络（Feddy），这是一个分布式的安全框架，用于从图谱序列中学习物体的表征。(1) 它聚集了来自当前图中附近物体的结构信息，以及来自前一个图中物体的动态信息。它使用自监督的方法来预测物体的运动轨迹。(2) 它是以联邦学习的方式进行训练的。位于中心的服务器将模型发送给用户设备。各个用户设备上的本地模型进行学习，并定期将它们的学习结果发送到**服务器，而不需要将用户的数据暴露给服务器。(3) 研究表明，在服务器进行加权平均后，广播给客户进行模型同步时，聚集的参数可以被检查，尽管是解密的。 ↩
Two important characteristics of contemporary wireless networks: (i) the network may contain heterogeneous communication/computation resources, while (ii) there may be significant overlaps in devices' local data distributions. In this work, we develop a novel optimization methodology that jointly accounts for these factors via intelligent device sampling complemented by device-to-device (D2D) offloading. Our optimization aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy subject to realistic constraints on the network topology and device capabilities. Theoretical analysis of the D2D offloading subproblem leads to new FedL convergence bounds and an efficient sequential convex optimizer. Using this result, we develop a sampling methodology based on graph convolutional networks (GCNs) which learns the relationship between network attributes, sampled nodes, and resulting offloading that maximizes FedL accuracy. 当代无线网络的两个重要特征：( i )网络中可能包含异构的通信/计算资源( ii )设备的本地数据分布可能存在显著的重叠。在这项工作中，我们开发了一种新的优化方法，通过智能设备采样和设备到设备(D2D)卸载来共同考虑这些因素。我们的优化目标是在网络拓扑和设备能力的现实约束下，选择采样节点和数据卸载配置的最佳组合，以最大化FedL训练精度。对D2D卸载子问题的理论分析得到了新的FedL收敛界和一个有效的序列凸优化器。利用这一结果，我们开发了一种基于图卷积网络(GCN)的采样方法，该方法学习网络属性、采样节点和结果卸载之间的关系，从而最大化FedL的准确性。 ↩ ↩²
Graphs can also be regarded as a special type of data samples. We analyze real-world graphs from different domains to confirm that they indeed share certain graph properties that are statistically significant compared with random graphs. However, we also find that different sets of graphs, even from the same domain or same dataset, are non-IID regarding both graph structures and node features. A graph clustered federated learning (GCFL) framework that dynamically finds clusters of local systems based on the gradients of GNNs, and theoretically justify that such clusters can reduce the structure and feature heterogeneity among graphs owned by the local systems. Moreover, we observe the gradients of GNNs to be rather fluctuating in GCFL which impedes high-quality clustering, and design a gradient sequence-based clustering mechanism based on dynamic time warping (GCFL+). 图也可以看作是一种特殊类型的数据样本。我们分析来自不同领域的真实图，以确认它们确实共享某些与随机图形相比具有统计意义的图属性。然而，我们也发现不同的图集，即使来自相同的域或相同的数据集，在图结构和节点特性方面都是非IID的。图聚类联邦学习(GCFL)框架，基于GNNs的梯度动态地找到本地系统的集群，并从理论上证明这样的集群可以减少本地系统所拥有的图之间的结构和特征异构性。此外，我们观察到GNNs的梯度在GCFL中波动较大，阻碍了高质量的聚类，并设计了基于动态时间规整的梯度序列聚类机制(GCFL+)。 ↩ ↩²
In this work, towards the novel yet realistic setting of subgraph federated learning, we propose two major techniques: (1) FedSage, which trains a GraphSage model based on FedAvg to integrate node features, link structures, and task labels on multiple local subgraphs; (2) FedSage+, which trains a missing neighbor generator along FedSage to deal with missing links across local subgraphs. 在本工作中，针对子图联邦学习的新颖而现实的设置，我们提出了两个主要技术：(1) FedSage，它基于FedAvg训练一个GraphSage模型，以整合多个局部子图上的节点特征、链接结构和任务标签；(2) FedSage +，它沿着FedSage训练一个缺失的邻居生成器，以处理跨本地子图的缺失链接。 ↩ ↩²
Cross-Node Federated Graph Neural Network (CNFGNN) , a federated spatio-temporal model, which explicitly encodes the underlying graph structure using graph neural network (GNN)-based architecture under the constraint of cross-node federated learning, which requires that data in a network of nodes is generated locally on each node and remains decentralized. CNFGNN operates by disentangling the temporal dynamics modeling on devices and spatial dynamics on the server, utilizing alternating optimization to reduce the communication cost, facilitating computations on the edge devices. 跨节点联邦图神经网络（CNFGNN），是一个联邦时空模型，在跨节点联邦学习的约束下，使用基于图神经网络（GNN）的架构对底层图结构进行显式编码，这要求节点网络中的数据是在每个节点上本地生成的，并保持分散。CNFGNN通过分解设备上的时间动态建模和服务器上的空间动态来运作，利用交替优化来降低通信成本，促进边缘设备的计算。 ↩ ↩²
A novel decentralized scalable learning framework, Federated Knowledge Graphs Embedding (FKGE), where embeddings from different knowledge graphs can be learnt in an asynchronous and peer-to-peer manner while being privacy-preserving. FKGE exploits adversarial generation between pairs of knowledge graphs to translate identical entities and relations of different domains into near embedding spaces. In order to protect the privacy of the training data, FKGE further implements a privacy-preserving neural network structure to guarantee no raw data leakage. 一种新颖的去中心化可扩展学习框架，联邦知识图谱嵌入(FKGE)，其中来自不同知识图谱的嵌入可以以异步和对等的方式学习，同时保持隐私。FKGE利用成对知识图谱之间的对抗生成，将不同领域的相同实体和关系转换到临近嵌入空间。为了保护训练数据的隐私，FKGE进一步实现了一个保护隐私的神经网络结构，以保证原始数据不会泄露。 ↩
A new Decentralized Federated Graph Neural Network (D-FedGNN for short) which allows multiple participants to train a graph neural network model without a centralized server. Specifically, D-FedGNN uses a decentralized parallel stochastic gradient descent algorithm DP-SGD to train the graph neural network model in a peer-to-peer network structure. To protect privacy during model aggregation, D-FedGNN introduces the Diffie-Hellman key exchange method to achieve secure model aggregation between clients. 一个新的去中心化的联邦图神经网络(简称D-FedGNN)允许多个参与者在没有中心化服务器的情况下训练一个图神经网络模型。具体地，D-FedGNN采用去中心化的并行随机梯度下降算法DP-SGD在对等网络结构中训练图神经网络模型。为了保护模型聚合过程中的隐私，D-FedGNN引入了Diffie-Hellman密钥交换方法来实现客户端之间的安全模型聚合。 ↩
We study the vertical and horizontal settings for federated learning on graph data. We propose FedSGC to train the Simple Graph Convolution model under three data split scenarios. 我们研究了图数据上联邦学习的横向和纵向设置。我们提出FedSGC在三种数据分割场景下训练简单图卷积模型。 ↩
A holistic collaborative and privacy-preserving FL framework, namely FL-DISCO, which integrates GAN and GNN to generate molecular graphs. 集成GAN和GNN生成分子图的整体协作和隐私保护FL框架FL-DISCO。 ↩
We introduce a differential privacy-based adjacency matrix preserving approach for protecting the topological information. We also propose an adjacency matrix aggregation approach to allow local GNN-based models to access the global network for a better training effect. Furthermore, we propose a GNN-based model named attention-based spatial-temporal graph neural networks (ASTGNN) for traffic speed forecasting. We integrate the proposed federated learning framework and ASTGNN as FASTGNN for traffic speed forecasting. 我们提出了一种基于差分隐私的邻接矩阵保护方法来保护拓扑信息。我们还提出了一种邻接矩阵聚合方法，允许基于局部GNN的模型访问全局网络，以获得更好的训练效果。此外，我们提出了一个基于GNN的模型，称为基于注意力的时空图神经网络(ASTGNN)的交通速度预测。我们将提出的联邦学习框架和ASTGNN集成为FASTGNN用于交通速度预测。 ↩
In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this paper introduces a framework for empowering FL using Direct Acyclic Graph (DAG)-based blockchain systematically (DAG-FL). 为了解决FL中的设备不同步和异常检测问题，同时避免区块链带来的额外资源消耗，本文提出了一种基于直接无环图(DAG, Direct Acyclic Graph)的区块链系统为FL赋能的框架(DAG-FL)。 ↩
In this paper, we introduce federated setting to keep Multi-Source KGs' privacy without triple transferring between KGs(Knowledge graphs) and apply it in embedding knowledge graph, a typical method which have proven effective for KGC(Knowledge Graph Completion) in the past decade. We propose a Federated Knowledge Graph Embedding framework FedE, focusing on learning knowledge graph embeddings by aggregating locally-computed updates. 在本文中，我们引入联邦设置来保持多源KGs的隐私，而不需要在KGs (知识图谱)之间传输三元组，并将其应用于知识图谱嵌入（这是一个典型的方法，在过去的十年中已证明对KGC(知识图谱补全)有效）。我们提出了一个联邦知识图谱嵌入框架FedE，重点是通过聚合本地计算的更新来学习知识图谱嵌入。 ↩
A new federated framework FKE for representation learning of knowledge graphs to deal with the problem of privacy protection and heterogeneous data. 一种新的联邦框架 FKE，用于知识图谱的表示学习，以处理隐私保护和异构数据的问题。 ↩
GFL, A private multi-server federated learning scheme, which we call graph federated learning. We use cryptographic and differential privacy concepts to privatize the federated learning algorithm over a graph structure. We further show under convexity and Lipschitz conditions, that the privatized process matches the performance of the non-private algorithm. GFL，一种私有的多服务器联邦学习方案，我们称之为图联邦学习。我们使用密码学和差分隐私概念将联邦学习算法私有化在图结构上。我们进一步表明在凸性和 Lipschitz 条件下，私有化过程与非私有算法的性能相匹配。 ↩
A novel framework Fedrated Social recommendation with Graph neural network (FeSoG). Firstly, FeSoG adopts relational attention and aggregation to handle heterogeneity. Secondly, FeSoG infers user embeddings using local data to retain personalization.The proposed model employs pseudo-labeling techniques with item sampling to protect the privacy and enhance training. 一种带有图神经网络 (FeSoG) 的新框架联邦社交推荐。首先，FeSoG 采用关系注意力和聚合来处理异质性。其次，FeSoG 使用本地数据推断用户嵌入以保留个性化。所提出的模型采用带有项目采样的伪标签技术来保护隐私并增强训练。 ↩
FedGraphNN, an open FL benchmark system that can facilitate research on federated GNNs. FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support. FedGraphNN是一个开放的FL基准系统，可以方便地进行联邦GNN的研究。FedGraphNN建立在图FL的统一提法之上，包含来自不同领域的广泛数据集、流行的GNN模型和FL算法，具有安全高效的系统支持。 ↩
The connectional brain template (CBT) is a compact representation (i.e., a single connectivity matrix) multi-view brain networks of a given population. CBTs are especially very powerful tools in brain dysconnectivity diagnosis as well as holistic brain mapping if they are learned properly – i.e., occupy the center of the given population. We propose the first federated connectional brain template learning (Fed-CBT) framework to learn how to integrate multi-view brain connectomic datasets collected by different hospitals into a single representative connectivity map. First, we choose a random fraction of hospitals to train our global model. Next, all hospitals send their model weights to the server to aggregate them. We also introduce a weighting method for aggregating model weights to take full benefit from all hospitals. Our model to the best of our knowledge is the first and only federated pipeline to estimate connectional brain templates using graph neural networks. 连接脑模板(CBT)是一个给定人群的紧凑表示(即,单个连接矩阵)多视图脑网络。CBTs在大脑障碍诊断和整体大脑映射中特别是非常强大的工具，如果它们被正确地学习- -即占据给定人群的中心。我们提出了第一个联邦连接脑模板学习( Fed-CBT )框架来学习如何将不同医院收集的多视角脑连接组学数据集整合成一个单一的代表性连接图。首先，我们随机选择一部分医院来训练我们的全球模型。接下来，所有医院将其模型权重发送给服务器进行聚合。我们还介绍了一种加权方法，用于聚合模型权重，以充分受益于所有医院。据我们所知，我们的模型是第一个也是唯一一个使用图神经网络来估计连接大脑模板的联邦管道。 ↩
A novel Cluster-driven Graph Federated Learning (FedCG). In FedCG, clustering serves to address statistical heterogeneity, while Graph Convolutional Networks (GCNs) enable sharing knowledge across them. FedCG: i) identifies the domains via an FL-compliant clustering and instantiates domain-specific modules (residual branches) for each domain; ii) connects the domain-specific modules through a GCN at training to learn the interactions among domains and share knowledge; and iii) learns to cluster unsupervised via teacher-student classifier-training iterations and to address novel unseen test domains via their domain soft-assignment scores. 一种新颖的集群驱动的图联邦学习（FedCG）。在 FedCG 中，聚类用于解决统计异质性，而图卷积网络 (GCN) 可以在它们之间共享知识。 FedCG：i）通过符合 FL 的集群识别域，并为每个域实例化特定于域的模块（剩余分支）； ii) 在训练时通过 GCN 连接特定领域的模块，以学习领域之间的交互并共享知识； iii）通过教师-学生分类器训练迭代学习无监督聚类，并通过其域软分配分数解决新的未知测试域。 ↩
Graph neural network (GNN) is widely used for recommendation to model high-order interactions between users and items.We propose a federated framework for privacy-preserving GNN-based recommendation, which can collectively train GNN models from decentralized user data and meanwhile exploit high-order user-item interaction information with privacy well protected. 图神经网络（GNN）被广泛用于推荐，以对用户和项目之间的高阶交互进行建模。我们提出了一种基于隐私保护的基于 GNN 的推荐的联邦框架，它可以从分散的用户数据集中训练 GNN 模型，同时利用高阶 - 订购用户-项目交互信息，隐私得到很好的保护。 ↩
We study the problem of how to efficiently learn a model in a peer-to-peer system with non-iid client data. We propose a method named Performance-Based Neighbor Selection (PENS) where clients with similar data distributions detect each other and cooperate by evaluating their training losses on each other's data to learn a model suitable for the local data distribution. 我们研究如何在具有非独立同分布客户端数据的对等系统中高效地学习模型的问题。我们提出了一种名为基于性能的邻居选择（Performance-Based Neighbor Selection，PENS）的方法，具有相似数据分布的客户端通过评估彼此数据的训练损失来相互检测和合作，从而学习适合本地数据分布的模型。 ↩
We study federated graph learning (FGL) under the cross-silo setting where several servers are connected by a wide-area network, with the objective of improving the Quality-of-Service (QoS) of graph learning tasks. Glint, a decentralized federated graph learning system with two novel designs: network traffic throttling and priority-based flows scheduling. 我们研究了跨孤岛设置下的联邦图学习（FGL），其中多台服务器通过广域网连接，目的是提高图学习任务的服务质量（QoS）。 Glint，一个分散的联邦图学习系统，具有两种新颖的设计：网络流量节流和基于优先级的流调度。 ↩
A novel distributed scalable federated graph neural network (FGNN) to solve the cross-graph node classification problem. We add PATE mechanism into the domain adversarial neural network (DANN) to construct a cross-network node classification model, and extract effective information from node features of source and target graphs for encryption and spatial alignment. Moreover, we use a one-to-one approach to construct cross-graph node classification models for multiple source graphs and the target graph. Federated learning is used to train the model jointly through multi-party cooperation to complete the target graph node classification task. 一种新颖的分布式可扩展联邦图神经网络 (FGNN)，用于解决跨图节点分类问题。我们在域对抗神经网络（DANN）中加入PATE机制，构建跨网络节点分类模型，从源图和目标图的节点特征中提取有效信息进行加密和空间对齐。此外，我们使用一对一的方法为多个源图和目标图构建跨图节点分类模型。联邦学习用于通过多方合作共同训练模型，完成目标图节点分类任务。 ↩
Human Activity Recognition (HAR) from sensor measurements is still challenging due to noisy or lack of la-belled examples and issues concerning data privacy. We propose a novel algorithm GraFeHTy, a Graph Convolution Network (GCN) trained in a federated setting. We construct a similarity graph from sensor measurements for each user and apply a GCN to perform semi-supervised classification of human activities by leveraging inter-relatedness and closeness of activities. 由于噪声或缺乏标记示例以及有关数据隐私的问题，来自传感器测量的人类活动识别 (HAR) 仍然具有挑战性。我们提出了一种新的算法 GraFeHTy，一种在联邦设置中训练的图卷积网络 (GCN)。我们从每个用户的传感器测量中构建相似图，并应用 GCN 通过利用活动的相互关联性和密切性来执行人类活动的半监督分类。 ↩
The aim of this work is to develop a fully-distributed algorithmic framework for training graph convolutional networks (GCNs). The proposed method is able to exploit the meaningful relational structure of the input data, which are collected by a set of agents that communicate over a sparse network topology. After formulating the centralized GCN training problem, we first show how to make inference in a distributed scenario where the underlying data graph is split among different agents. Then, we propose a distributed gradient descent procedure to solve the GCN training problem. The resulting model distributes computation along three lines: during inference, during back-propagation, and during optimization. Convergence to stationary solutions of the GCN training problem is also established under mild conditions. Finally, we propose an optimization criterion to design the communication topology between agents in order to match with the graph describing data relationships. 这项工作的目的是开发一个用于训练图卷积网络（GCN）的完全分布式算法框架。所提出的方法能够利用输入数据的有意义的关系结构，这些数据由一组通过稀疏网络拓扑进行通信的代理收集。在制定了集中式 GCN 训练问题之后，我们首先展示了如何在底层数据图在不同代理之间拆分的分布式场景中进行推理。然后，我们提出了一种分布式梯度下降程序来解决 GCN 训练问题。生成的模型沿三条线分布计算：推理期间、反向传播期间和优化期间。 GCN 训练问题的平稳解的收敛性也在温和条件下建立。最后，我们提出了一种优化标准来设计代理之间的通信拓扑，以便与描述数据关系的图相匹配。 ↩
We focus on improving the communication efficiency for fully decentralized federated learning (DFL) over a graph, where the algorithm performs local updates for several iterations and then enables communications among the nodes. 我们专注于提高图上完全分散的联邦学习（DFL）的通信效率，其中算法执行多次迭代的本地更新，然后实现节点之间的通信。 ↩
An Automated Separated-Federated Graph Neural Network (ASFGNN) learning paradigm. ASFGNN consists of two main components, i.e., the training of GNN and the tuning of hyper-parameters. Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally. To handle the time-consuming parameter tuning problem, we leverage Bayesian optimization technique to automatically tune the hyper-parameters of all the clients. 自动分离联邦图神经网络( ASFGNN )学习范式。ASFGNN由两个主要部分组成，即GNN的训练和超参数的调整。具体来说，为了解决数据Non - IID问题，我们首先提出了分离联邦GNN学习模型，将GNN的训练解耦为两个部分：由客户端单独完成的消息传递部分和由客户端联邦学习的损失计算部分。为了处理耗时的参数调优问题，我们利用贝叶斯优化技术自动调优所有客户端的超参数。 ↩
Communication is a critical enabler of large-scale FL due to significant amount of model information exchanged among edge devices. In this paper, we consider a network of wireless devices sharing a common fading wireless channel for the deployment of FL. Each device holds a generally distinct training set, and communication typically takes place in a Device-to-Device (D2D) manner. In the ideal case in which all devices within communication range can communicate simultaneously and noiselessly, a standard protocol that is guaranteed to converge to an optimal solution of the global empirical risk minimization problem under convexity and connectivity assumptions is Decentralized Stochastic Gradient Descent (DSGD). DSGD integrates local SGD steps with periodic consensus averages that require communication between neighboring devices. In this paper, wireless protocols are proposed that implement DSGD by accounting for the presence of path loss, fading, blockages, and mutual interference. The proposed protocols are based on graph coloring for scheduling and on both digital and analog transmission strategies at the physical layer, with the latter leveraging over-the-air computing via sparsity-based recovery. 由于边缘设备之间交换了大量模型信息，因此通信是大规模 FL 的关键推动力。在本文中，我们考虑了一个无线设备网络，该网络共享一个共同的衰落无线信道来部署 FL。每个设备都拥有一个通常不同的训练集，并且通信通常以设备到设备 (D2D) 的方式进行。在通信范围内的所有设备可以同时无噪声地通信的理想情况下，保证在凸性和连通性假设下收敛到全局经验风险最小化问题的最优解的标准协议是分散随机梯度下降（DSGD）。 DSGD 将本地 SGD 步骤与需要相邻设备之间通信的周期性共识平均值集成在一起。在本文中，提出了通过考虑路径损耗、衰落、阻塞和相互干扰的存在来实现 DSGD 的无线协议。所提出的协议基于用于调度的图形着色以及物理层的数字和模拟传输策略，后者通过基于稀疏性的恢复利用空中计算。 ↩
We propose a similarity-based graph neural network model, SGNN, which captures the structure information of nodes precisely in node classification tasks. It also takes advantage of the thought of federated learning to hide the original information from different data sources to protect users' privacy. We use deep graph neural network with convolutional layers and dense layers to classify the nodes based on their structures and features. 我们提出了一种基于相似度的图神经网络模型 SGNN，它在节点分类任务中精确地捕获节点的结构信息。它还利用联邦学习的**，对不同数据源隐藏原始信息，保护用户隐私。我们使用具有卷积层和密集层的深度图神经网络根据节点的结构和特征对节点进行分类。 ↩
To detect financial misconduct, A methodology to share key information across institutions by using a federated graph learning platform that enables us to build more accurate machine learning models by leveraging federated learning and also graph learning approaches. We demonstrated that our federated model outperforms local model by 20% with the UK FCA TechSprint data set. 为了检测财务不当行为，一种通过使用联邦图学习平台在机构间共享关键信息的方法，使我们能够通过利用联邦学习和图学习方法来构建更准确的机器学习模型。我们证明了我们的联邦模型在英国 FCA TechSprint 数据集上的性能优于本地模型 20%。 ↩
We aim at solving a binary supervised classification problem to predict hospitalizations for cardiac events using a distributed algorithm. We focus on the soft-margin l1-regularized sparse Support Vector Machine (sSVM) classifier. We develop an iterative cluster Primal Dual Splitting (cPDS) algorithm for solving the large-scale sSVM problem in a decentralized fashion. 我们的目标是解决一个二元监督分类问题，以使用分布式算法预测心脏事件的住院情况。我们专注于软边距 l1 正则化稀疏支持向量机 (sSVM) 分类器。我们开发了一种迭代集群 Primal Dual Splitting (cPDS) 算法，用于以分散的方式解决大规模 sSVM 问题。 ↩
We first formulate the Graph Federated Learning (GFL) problem that unifies LoG(Learning on Graphs) and FL(Federated Learning) in multi-client systems and then propose sharing hidden representation instead of the raw data of neighbors to protect data privacy as a solution. To overcome the biased gradient problem in GFL, we provide a gradient estimation method and its convergence analysis under the non-convex objective. 我们首先在多客户机系统中统一LoG(在图上学习)和FL (Federation Learning)的图联邦学习(Graph Federation Learning，GFL)问题，然后提出共享隐藏表示代替邻居的原始数据以保护数据隐私作为解决方案。为了克服GFL中的有偏梯度问题，我们给出了非凸目标下的梯度估计方法及其收敛性分析。 ↩
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FedEgo, a federated graph learning framework based on ego-graphs, where each client will train their local models while also contributing to the training of a global model. FedEgo applies GraphSAGE over ego-graphs to make full use of the structure information and utilizes Mixup for privacy concerns. To deal with the statistical heterogeneity, we integrate personalization into learning and propose an adaptive mixing coefficient strategy that enables clients to achieve their optimal personalization. FedEgo是一个基于自中心图的联邦图学习框架，每个客户端将训练他们的本地模型，同时也为全局模型的训练作出贡献。FedEgo在自中心图上应用GraphSAGE来充分利用结构信息，并利用Mixup来解决隐私问题。为了处理统计上的异质性，我们将个性化整合到学习中，并提出了一个自适应混合系数策略，使客户能够实现其最佳的个性化。 ↩
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An efficient and privacy-preserving vertical federated tree boosting framework, namely SGBoost, where multiple participants can collaboratively perform model training and query without staying online all the time. Specifically, we first design secure bucket sharing and best split finding algorithms, with which the global tree model can be constructed over vertically partitioned data; meanwhile, the privacy of training data can be well guaranteed. Then, we design an oblivious query algorithm to utilize the trained model without leaking any query data or results. Moreover, SGBoost does not require multi-round interactions between participants, significantly improving the system efficiency. Detailed security analysis shows that SGBoost can well guarantee the privacy of raw data, weights, buckets, and split information. 一个高效且保护隐私的纵向联邦树提升框架，即SGBoost，多个参与者可以协同进行模型训练和查询，而无需一直保持在线。具体来说，我们首先设计了安全的桶共享和最佳分割寻找算法，通过这些算法可以在垂直分割的数据上构建全局树模型；同时，训练数据的隐私可以得到很好的保证。然后，我们设计了一个遗忘查询算法来利用训练好的模型，而不泄露任何查询数据或结果。此外，SGBoost不需要参与者之间的多轮互动，大大提高了系统的效率。详细的安全分析表明，SGBoost可以很好地保证原始数据、权重、桶和分割信息的隐私。 ↩
iFedCrowd (incentive-boosted Federated Crowdsourcing), to manage the privacy and quality of crowdsourcing projects. iFedCrowd allows participants to locally process sensitive data and only upload encrypted training models, and then aggregates the model parameters to build a shared server model to protect data privacy. To motivate workers to build a high-quality global model in an efficacy way, we introduce an incentive mechanism that encourages workers to constantly collect fresh data to train accurate client models and boosts the global model training. We model the incentive-based interaction between the crowdsourcing platform and participating workers as a Stackelberg game, in which each side maximizes its own profit. We derive the Nash Equilibrium of the game to find the optimal solutions for the two sides. iFedCrowd（激励促进的联合众包），管理众包项目的隐私和质量。iFedCrowd允许参与者在本地处理敏感数据，只上传加密的训练模型，然后汇总模型参数，建立一个共享的服务器模型，保护数据隐私。为了激励工人以效能的方式建立高质量的全局模型，我们引入了一种激励机制，鼓励工人不断收集新鲜数据来训练准确的客户模型，并促进全局模型的训练。我们将众包平台和参与工人之间基于激励的互动建模为Stackelberg博弈，其中每一方都最大化自己的利润。我们推导出博弈的纳什均衡，以找到双方的最佳解决方案。 ↩ ↩²
OpBoost. Three order-preserving desensitization algorithms satisfying a variant of LDP called distance-based LDP (dLDP) are designed to desensitize the training data. In particular, we optimize the dLDP definition and study efficient sampling distributions to further improve the accuracy and efficiency of the proposed algorithms. The proposed algorithms provide a trade-off between the privacy of pairs with large distance and the utility of desensitized values. OpBoost。设计了三种满足LDP变体的保序脱敏算法，称为基于距离的LDP（dLDP），以使训练数据脱敏。特别是，我们优化了dLDP的定义，并研究了有效的采样分布，以进一步提高拟议算法的准确性和效率。所提出的算法在大距离的配对隐私和脱敏值的效用之间进行了权衡。 ↩ ↩²
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Federated functional gradient boosting (FFGB). Under appropriate assumptions on the weak learning oracle, the FFGB algorithm is proved to efficiently converge to certain neighborhoods of the global optimum. The radii of these neighborhoods depend upon the level of heterogeneity measured via the total variation distance and the much tighter Wasserstein-1 distance, and diminish to zero as the setting becomes more homogeneous. ↩
Federated Random Forests (FRF) models, focusing particularly on the heterogeneity within and between datasets. 联邦随机森林（FRF）模型，特别关注数据集内部和之间的异质性。 ↩
This paper proposes FL algorithms that build federated models without relying on gradient descent-based methods. Specifically, we leverage distributed versions of the AdaBoost algorithm to acquire strong federated models. In contrast with previous approaches, our proposal does not put any constraint on the client-side learning models. 不依赖基于梯度下降的方法建立联邦模型的FL算法。具体来说，我们利用AdaBoost算法的分布式版本来获得强大的联邦模型。与之前的方法相比，我们没有对客户端的学习模型施加任何约束。 ↩
Federated Forest , which is a lossless learning model of the traditional random forest method, i.e., achieving the same level of accuracy as the non-privacy-preserving approach. Based on it, we developed a secure cross-regional machine learning system that allows a learning process to be jointly trained over different regions’ clients with the same user samples but different attribute sets, processing the data stored in each of them without exchanging their raw data. A novel prediction algorithm was also proposed which could largely reduce the communication overhead. Federated Forest ，是传统随机森林方法的无损学习模型，即达到与非隐私保护方法相同的准确度。在此基础上，我们开发了一个安全的跨区域机器学习系统，允许在具有相同用户样本但不同属性集的不同区域的客户端上联邦训练一个学习过程，处理存储在每个客户端的数据，而不交换其原始数据。还提出了一种新的预测算法，可以在很大程度上减少通信开销。 ↩
Fed-GBM (Federated Gradient Boosting Machines), a cost-effective collaborative learning framework, consisting of two-stage voting and node-level parallelism, to address the problems in co-modelling for Non-intrusive load monitoring (NILM). Fed-GBM(联邦梯度提升)是一个具有成本效益的协作学习框架，由两阶段投票和节点级并行组成，用于解决非侵入式负载监测(NILM)中的协同建模问题。 ↩
A verifiable privacy-preserving scheme (VPRF) based on vertical federated Random forest, in which the users are dynamic change. First, we design homomorphic comparison and voting statistics algorithms based on multikey homomorphic encryption for privacy preservation. Then, we propose a multiclient delegated computing verification algorithm to make up for the disadvantage that the above algorithms cannot verify data integrity. 一个基于纵向联邦随机森林的可验证的隐私保护方案（VPRF），其中的用户是动态变化的。首先，我们设计了基于多键同态加密的同态比较和投票统计算法来保护隐私。然后，我们提出了一种多客户委托计算验证算法，以弥补上述算法不能验证数据完整性的缺点。 ↩
A novel federated ensemble classification algorithm for horizontally partitioned data, namely Boosting-based Federated Random Forest (BOFRF), which not only increases the predictive power of all participating sites, but also provides significantly high improvement on the predictive power of sites having unsuccessful local models. We implement a federated version of random forest, which is a well-known bagging algorithm, by adapting the idea of boosting to it. We introduce a novel aggregation and weight calculation methodology that assigns weights to local classifiers based on their classification performance at each site without increasing the communication or computation cost. 一种针对横向划分数据的新型联邦集成分类算法，即基于 Boosting 的联邦随机森林 (BOFRF)，它不仅提高了所有参与站点的预测能力，而且显着提高了局部模型不成功的站点的预测能力 . 我们通过采用 boosting 的**来实现一个联邦版本的随机森林，这是一种众所周知的 bagging 算法。我们引入了一种新颖的聚合和权重计算方法，该方法根据本地分类器在每个站点的分类性能为它们分配权重，而不会增加通信或计算成本。 ↩
Efficient FL for GBDT (eFL-Boost), which minimizes accuracy loss, communication costs, and information leakage. The proposed scheme focuses on appropriate allocation of local computation (performed individually by each organization) and global computation (performed cooperatively by all organizations) when updating a model. A tree structure is determined locally at one of the organizations, and leaf weights are calculated globally by aggregating the local gradients of all organizations. Specifically, eFL-Boost requires only three communications per update, and only statistical information that has low privacy risk is leaked to other organizations. 针对GBDT的高效FL（eFL-Boost），将精度损失、通信成本和信息泄露降到最低。该方案的重点是在更新模型时适当分配局部计算（由每个组织单独执行）和全局计算（由所有组织合作执行）。树状结构由其中一个组织在本地确定，而叶子的权重则由所有组织的本地梯度汇总后在全局计算。具体来说，eFL-Boost每次更新只需要三次通信，而且只有具有低隐私风险的统计信息才会泄露给其他组织。 ↩
MP-FedXGB, a lossless multi-party federated XGB learning framework is proposed with a security guarantee, which reshapes the XGBoost's split criterion calculation process under a secret sharing setting and solves the leaf weight calculation problem by leveraging distributed optimization. MP-FedXGB是一个无损的多方联邦XGB学习框架，它在秘密共享的环境下重塑了XGBoost的分割准则计算过程，并通过利用分布式优化解决了叶子权重计算问题。 ↩
Random Forest Based on Federated Learning for Intrusion Detection 使用联邦随机森林做入侵检测 ↩
A federated decision tree-based random forest algorithm where a small number of organizations or industry companies collaboratively build models. 一个基于联邦决策树的随机森林算法，由少数组织或行业公司合作建立模型。 ↩
VF2Boost, a novel and efficient vertical federated GBDT system. First, to handle the deficiency caused by frequent mutual-waiting in federated training, we propose a concurrent training protocol to reduce the idle periods. Second, to speed up the cryptography operations, we analyze the characteristics of the algorithm and propose customized operations. Empirical results show that our system can be 12.8-18.9 times faster than the existing vertical federated implementations and support much larger datasets. VF2Boost，一个新颖而高效的纵向联邦GBDT系统。首先，为了处理联邦训练中频繁的相互等待造成的缺陷，我们提出了一个并发训练协议来减少空闲期。第二，为了加快密码学操作，我们分析了算法的特点，并提出了定制的操作。经验结果表明，我们的系统可以比现有的纵向联邦实现快12.8-18.9倍，并支持更大的数据集。我们将保证公平性的客户选择建模为一个Lyapunov优化问题，然后提出一个基于C2MAB的方法来估计每个客户和服务器之间的模型交换时间，在此基础上，我们设计了一个保证公平性的算法，即RBCS-F来解决问题。 ↩ ↩²
SecureBoost, a novel lossless privacy-preserving tree-boosting system. SecureBoost first conducts entity alignment under a privacy-preserving protocol and then constructs boosting trees across multiple parties with a carefully designed encryption strategy. This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set. SecureBoost是一种新型的无损隐私保护的提升树系统。SecureBoost首先在一个保护隐私的协议下进行实体对齐，然后通过精心设计的加密策略在多方之间构建提升树。这种联邦学习系统允许学习过程在具有共同用户样本但不同特征集的多方联邦进行，这相当于一个纵向分割的数据集。 ↩
A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System 基于区块链的联邦森林用于支持SDN的车载网络入侵检测系统 ↩
An improved gradient boosting decision tree (GBDT) federated ensemble learning method is proposed, which takes the average gradient of similar samples and its own gradient as a new gradient to improve the accuracy of the local model. Different ensemble learning methods are used to integrate the parameters of the local model, thus improving the accuracy of the updated global model. 提出了一种改进的梯度提升决策树（GBDT）联邦集合学习方法，该方法将相似样本的平均梯度和自身的梯度作为新的梯度来提高局部模型的精度。采用不同的集合学习方法来整合局部模型的参数，从而提高更新的全局模型的精度。 ↩
Decision tree ensembles such as gradient boosting decision trees (GBDT) and random forest are widely applied powerful models with high interpretability and modeling efficiency. However, state-of-art framework for decision tree ensembles in vertical federated learning frameworks adapt anonymous features to avoid possible data breaches, makes the interpretability of the model compromised. Fed-EINI make a problem analysis about the necessity of disclosure meanings of feature to Guest Party in vertical federated learning. Fed-EINI protect data privacy and allow the disclosure of feature meaning by concealing decision paths and adapt a communication-efficient secure computation method for inference outputs. 集成决策树，如梯度提升决策树（GBDT）和随机森林，是被广泛应用的强大模型，具有较高的可解释性和建模效率。然而，纵向联邦学习框架中的决策树群的先进框架适应匿名特征以避免可能的数据泄露，使得模型的可解释性受到影响。Fed-EINI对纵向联邦学习中向客人方披露特征含义的必要性进行了问题分析。Fed-EINI通过隐藏决策路径来保护数据隐私，并允许披露特征含义，同时为推理输出适应一种通信效率高的安全计算方法。 ↩
Propose a new tree-boosting method, named Gradient Boosting Forest (GBF), where the single decision tree in each gradient boosting round of GBDT is replaced by a set of trees trained from different subsets of the training data (referred to as a forest), which enables training GBDT in Federated Learning scenarios. We empirically prove that GBF outperforms the existing GBDT methods in both centralized (GBF-Cen) and federated (GBF-Fed) cases. 我们提出了一种新的提升树方法，即梯度提升森林（GBF），在GBDT的每一轮梯度提升中，单一的决策树被一组从训练数据的不同子集训练出来的树（称为森林）所取代，这使得在联邦学习场景中可以训练GBDT。我们通过经验证明，GBF在集中式（GBF-Cen）和联邦式（GBF-Fed）情况下都优于现有的GBDT方法。 ↩
A privacy-preserving framework using Mondrian k-anonymity with decision trees for the horizontally partitioned data. 使用Mondrian K-匿名化的隐私保护框架，对横向分割的数据使用决策树建模。 ↩
AF-DNDF which extends DNDF (Deep Neural Decision Forests, which unites classification trees with the representation learning functionality from deep convolutional neural networks) with an asynchronous federated aggregation protocol. Based on the local quality of each classification tree, our architecture can select and combine the optimal groups of decision trees from multiple local devices. AF-DNDF，它将DNDF（深度神经决策森林，它将分类树与深度卷积神经网络的表征学习功能结合起来）与一个异步的联邦聚合协议进行了扩展。基于每个分类树的本地质量，我们的架构可以选择和组合来自多个本地设备的最佳决策树组。 ↩
Differential Privacy is used to obtain theoretically sound privacy guarantees against such inference attacks by noising the exchanged update vectors. However, the added noise is proportional to the model size which can be very large with modern neural networks. This can result in poor model quality. Compressive sensing is used to reduce the model size and hence increase model quality without sacrificing privacy. 差分隐私是通过对交换的更新向量进行噪声处理来获得理论上合理的隐私保证，以抵御这种推断攻击。然而，增加的噪声与模型大小成正比，而现代神经网络的模型大小可能非常大。这可能会导致模型质量不佳。压缩感知被用来减少模型大小，从而在不牺牲隐私的情况下提高模型质量。 ↩
A practical horizontal federated environment with relaxed privacy constraints. In this environment, a dishonest party might obtain some information about the other parties' data, but it is still impossible for the dishonest party to derive the actual raw data of other parties. Specifically, each party boosts a number of trees by exploiting similarity information based on locality-sensitive hashing. 一个具有宽松隐私约束的实用横向联邦环境。在这种环境中，不诚实的一方可能会获得其他方数据的一些信息，但不诚实的一方仍然不可能得出其他方的实际原始数据。具体来说，每一方通过利用基于位置敏感散列的相似性信息来提升一些树。 ↩ ↩²
Pivot, a novel solution for privacy preserving vertical decision tree training and prediction, ensuring that no intermediate information is disclosed other than those the clients have agreed to release (i.e., the final tree model and the prediction output). Pivot does not rely on any trusted third party and provides protection against a semi-honest adversary that may compromise m - 1 out of m clients. We further identify two privacy leakages when the trained decision tree model is released in plain-text and propose an enhanced protocol to mitigate them. The proposed solution can also be extended to tree ensemble models, e.g., random forest (RF) and gradient boosting decision tree (GBDT) by treating single decision trees as building blocks. Pivot，一个用于保护隐私的纵向决策树训练和预测的新颖解决方案，确保除了客户同意发布的信息（即最终的树模型和预测输出）外，没有任何中间信息被披露。Pivot不依赖任何受信任的第三方，并提供保护，防止半诚实的对手可能损害m个客户中的m-1。我们进一步确定了当训练好的决策树模型以明文形式发布时的两个隐私泄漏，并提出了一个增强的协议来缓解这些泄漏。通过将单个决策树作为构建块，所提出的解决方案也可以扩展到集成树模型，如随机森林（RF）和梯度提升决策树（GBDT）。 ↩ ↩²
FEDXGB, a federated extreme gradient boosting (XGBoost) scheme supporting forced aggregation. First, FEDXGB involves a new HE(homomorphic encryption) based secure aggregation scheme for FL. Then, FEDXGB extends FL to a new machine learning model by applying the secure aggregation scheme to the classification and regression tree building of XGBoost. FEDXGB，一个支持强制聚合的联邦极端梯度提升（XGBoost）方案。首先，FEDXGB涉及一个新的基于HE（同态加密）的FL的安全聚合方案。然后，FEDXGB通过将安全聚合方案应用于XGBoost的分类和回归树构建，将FL扩展到一个新的机器学习模型。 ↩
FedCluster, a novel federated learning framework with improved optimization efficiency, and investigate its theoretical convergence properties. The FedCluster groups the devices into multiple clusters that perform federated learning cyclically in each learning round. FedCluster是一个具有改进的优化效率的新型联邦学习框架，并研究其理论收敛特性。FedCluster将设备分成多个集群，在每一轮学习中循环进行联邦学习。 ↩
The proposed FL-XGBoost can train a sensitive task to be solved among different entities without revealing their own data. The proposed FL-XGBoost can achieve significant reduction in the number of communications between entities by exchanging decision tree models. FL-XGBoost可以训练一个敏感的任务，在不同的实体之间解决，而不透露他们自己的数据。所提出的FL-XGBoost可以通过交换决策树模型实现实体之间通信数量的大幅减少。 ↩
A bandwidth slicing algorithm in PONs(passive optical network) is introduced for efficient FL, in which bandwidth is reserved for the involved ONUs(optical network units) collaboratively and mapped into each polling cycle. 在PONs（无源光网络）中引入了一种高效的FL算法，即为参与的ONU（光网络单元）协同保留带宽并映射到每个轮询周期。 ↩
A distributed machine learning system based on local random forest algorithms created with shared decision trees through the blockchain. 一个基于本地随机森林算法的分布式机器学习系统通过区块链创建了共享决策树。 ↩
A decentralized redundant n-Cayley tree (DRC-tree) for federated learning. Explore the hierarchical structure of the n-Cayley tree to enhance the redundancy rate in federated learning to mitigate the impact of stragglers. In the DRC- tree structure, the fusion node serves as the root node, while all the worker devices are the intermediate tree nodes and leaves that formulated through a distributed message passing interface. the redundancy of workers is constructed layer by layer with a given redundancy branch degree. 用于联邦学习的分散冗余n-Cayley树（DRC-tree）。探索n-Cayley树的分层结构，提高联邦学习中的冗余率，以减轻散兵游勇的影响。在DRC-树结构中，融合节点作为根节点，而所有客户端设备是通过分布式消息传递接口制定的中间树节点和叶子。客户端的冗余度是以给定的冗余分支度逐层构建的。 ↩
Fed-sGBM, a federated soft gradient boosting machine framework applicable on the streaming data. Compared with traditional gradient boosting methods, where base learners are trained sequentially, each base learner in the proposed framework can be efficiently trained in a parallel and distributed fashion. Fed-sGBM是一个适用于流数据的联邦软梯度提升机框架。与传统的梯度提升方法相比，传统的梯度提升方法中的基础学习器是按顺序训练的，而拟议的框架中的每个基础学习器可以以平行和分布的方式有效地训练。 ↩
Deep neural decision forests (DNDF), combine the divide-and-conquer principle together with the property representation learning. By parameterizing the probability distributions in the prediction nodes of the forest, and include all trees of the forest in the loss function, a gradient of the whole forest can be computed which some/several federated learning algorithms utilize. 深度神经决策森林（DNDF），将分治策略与属性表示学习结合起来。通过对森林预测节点的概率分布进行参数化，并将森林中的所有树木纳入损失函数中，可以计算出整个森林的梯度，一些/一些联邦学习算法利用了这一梯度。 ↩
A federated tabular data augmentation method, named Fed-TDA. The core idea of Fed-TDA is to synthesize tabular data for data augmentation using some simple statistics (e.g., distributions of each column and global covariance). Specifically, we propose the multimodal distribution transformation and inverse cumulative distribution mapping respectively synthesize continuous and discrete columns in tabular data from a noise according to the pre-learned statistics. Furthermore, we theoretically analyze that our Fed-TDA not only preserves data privacy but also maintains the distribution of the original data and the correlation between columns. 一种名为Fed-TDA的联合表格式数据扩充方法。Fed-TDA的核心**是利用一些简单的统计数据（如每一列的分布和全局协方差）来合成表格数据进行数据扩增。具体来说，我们提出了多模态分布变换和反累积分布映射，分别根据预先学习的统计数据从噪声中合成表格数据的连续和离散列。此外，我们从理论上分析，我们的Fed-TDA不仅保留了数据隐私，而且还保持了原始数据的分布和列之间的相关性。 ↩
Most high-stakes applications of FL (e.g., legal and financial) use tabular data. Compared to the NLP and image domains, reconstruction of tabular data poses several unique challenges: (i) categorical features introduce a significantly more difficult mixed discrete-continuous optimization problem, (ii) the mix of categorical and continuous features causes high variance in the final reconstructions, and (iii) structured data makes it difficult for the adversary to judge reconstruction quality. In this work, we tackle these challenges and propose the first comprehensive reconstruction attack on tabular data, called TabLeak. TabLeak is based on three key ingredients: (i) a softmax structural prior, implicitly converting the mixed discrete-continuous optimization problem into an easier fully continuous one, (ii) a way to reduce the variance of our reconstructions through a pooled ensembling scheme exploiting the structure of tabular data, and (iii) an entropy measure which can successfully assess reconstruction quality. 大多数高风险的FL应用（例如，法律和金融）都使用表格数据。与NLP和图像领域相比，表格数据的重建带来了几个独特的挑战：（i）分类特征引入了一个明显更困难的混合离散-连续优化问题，（ii）分类和连续特征的混合导致最终重建的高差异，以及（iii）结构化数据使得对手很难判断重建质量。在这项工作中，我们解决了这些挑战，并提出了第一个针对表格数据的全面重建攻击，称为TabLeak。TabLeak是基于三个关键因素。(i) 一个softmax结构先验，隐含地将混合的离散-连续优化问题转换为一个更容易的完全连续问题，(ii) 一个通过利用表格数据结构的集合方案来减少我们重建的方差的方法，以及(iii) 一个可以成功评估重建质量的熵测量。 ↩
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A hybrid federated learning framework based on XGBoost, for distributed power prediction from real-time external features. In addition to introducing boosted trees to improve accuracy and interpretability, we combine horizontal and vertical federated learning, to address the scenario where features are scattered in local heterogeneous parties and samples are scattered in various local districts. Moreover, we design a dynamic task allocation scheme such that each party gets a fair share of information, and the computing power of each party can be fully leveraged to boost training efficiency. 一个基于XGBoost的混合联邦学习框架，用于从实时外部特征进行分布式电力预测。除了引入提升树来提高准确性和可解释性，我们还结合了横向和纵向的联邦学习，以解决特征分散在本地异质方和样本分散在不同本地区的情况。此外，我们设计了一个动态的任务分配方案，使每一方都能获得公平的信息份额，并能充分利用每一方的计算能力来提高训练效率。 ↩
Efficient XGBoost vertical federated learning. we proposed a novel batch homomorphic encryption method to cut the cost of encryption-related computation and transmission in nearly half. This is achieved by encoding the first-order derivative and the second-order derivative into a single number for encryption, ciphertext transmission, and homomorphic addition operations. The sum of multiple first-order derivatives and second-order derivatives can be simultaneously decoded from the sum of encoded values. 高效的XGBoost纵向联邦学习。我们提出了一种新颖的批量同态加密方法，将加密相关的计算和传输成本减少了近一半。这是通过将一阶导数和二阶导数编码为一个数字来实现的，用于加密、密码文本传输和同态加法操作。多个一阶导数和二阶导数的总和可以同时从编码值的总和中解密。 ↩
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Two variants of federated XGBoost with privacy guarantee: FedXGBoost-SMM and FedXGBoost-LDP. Our first protocol FedXGBoost-SMM deploys enhanced secure matrix multiplication method to preserve privacy with lossless accuracy and lower overhead than encryption-based techniques. Developed independently, the second protocol FedXGBoost-LDP is heuristically designed with noise perturbation for local differential privacy. 两种具有隐私保护的联邦XGBoost的变体：FedXGBoost-SMM和FedXGBoost-LDP。FedXGBoost-SMM部署了增强的安全矩阵乘法，以无损的精度和低于基于加密的技术的开销来保护隐私。第二个协议FedXGBoost-LDP以启发式方法设计的，带有噪声扰动，用于保护局部差分隐私。 ↩
FederBoost for private federated learning of gradient boosting decision trees (GBDT). It supports running GBDT over both horizontally and vertically partitioned data. The key observation for designing FederBoost is that the whole training process of GBDT relies on the order of the data instead of the values. Consequently, vertical FederBoost does not require any cryptographic operation and horizontal FederBoost only requires lightweight secure aggregation. FederBoost用于梯度提升决策树（GBDT）的私有联邦学习。它支持在横向和纵向分区的数据上运行GBDT。设计FederBoost的关键是，GBDT的整个训练过程依赖于数据的顺序而不是数值。因此，纵向FederBoost不需要任何加密操作，横向FederBoost只需要轻量级的安全聚合。 ↩
A horizontal federated XGBoost algorithm to solve the federated anomaly detection problem, where the anomaly detection aims to identify abnormalities from extremely unbalanced datasets and can be considered as a special classification problem. Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance. In particular, we introduce the virtual data sample by aggregating a group of users' data together at a single distributed node. 一个横向联邦XGBoost算法来解决联邦异常检测问题，其中异常检测的目的是从极不平衡的数据集中识别异常，可以被视为一个特殊的分类问题。我们提出的联邦XGBoost算法包含了数据聚合和稀疏的联邦更新过程，以平衡隐私和学习性能之间的权衡。特别是，我们通过将一组用户的数据聚集在一个分布式节点上，引入虚拟数据样本。 ↩
With the advent of deep learning and increasing use of brain MRIs, a great amount of interest has arisen in automated anomaly segmentation to improve clinical workflows; however, it is time-consuming and expensive to curate medical imaging. FedDis to collaboratively train an unsupervised deep convolutional autoencoder on 1,532 healthy magnetic resonance scans from four different institutions, and evaluate its performance in identifying pathologies such as multiple sclerosis, vascular lesions, and low- and high-grade tumours/glioblastoma on a total of 538 volumes from six different institutions. To mitigate the statistical heterogeneity among different institutions, we disentangle the parameter space into global (shape) and local (appearance). Four institutes jointly train shape parameters to model healthy brain anatomical structures. Every institute trains appearance parameters locally to allow for client-specific personalization of the global domain-invariant features. 随着深度学习的出现和脑 MRI 的使用越来越多，人们对自动异常分割以改善临床工作流程产生了极大的兴趣。然而，管理医学成像既耗时又昂贵。 FedDis 将在来自四个不同机构的 1,532 次健康磁共振扫描上协作训练一个无监督的深度卷积自动编码器，并评估其在总共 538 个机构中识别多发性硬化症、血管病变以及低级别和高级别肿瘤/胶质母细胞瘤等病理的性能来自六个不同机构的卷。为了减轻不同机构之间的统计异质性，我们将参数空间分解为全局（形状）和局部（外观）。四个研究所联邦训练形状参数来模拟健康的大脑解剖结构。每个机构都在本地训练外观参数，以允许对全局域不变特征进行客户特定的个性化。 ↩
This progress has emphasized that, from model development to model deployment, data play central roles. In this Review, we provide a data-centric view of the innovations and challenges that are defining ML for healthcare. We discuss deep generative models and federated learning as strategies to augment datasets for improved model performance, as well as the use of the more recent transformer models for handling larger datasets and enhancing the modelling of clinical text. We also discuss data-focused problems in the deployment of ML, emphasizing the need to efficiently deliver data to ML models for timely clinical predictions and to account for natural data shifts that can deteriorate model performance. 这一进展强调，从模型开发到模型部署，数据发挥着核心作用。在这篇评论中，我们提供了一个以数据为中心的观点，即定义医疗保健的ML的创新和挑战。我们讨论了深度生成模型和联邦学习，作为增强数据集以提高模型性能的策略，以及使用最近的转化器模型来处理更大的数据集和加强临床文本的建模。我们还讨论了ML部署中以数据为重点的问题，强调需要有效地将数据交付给ML模型，以便及时进行临床预测，并考虑到可能恶化模型性能的自然数据转移。 ↩
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This paper focuses on communication-efficient federated learning problem, and develops a novel distributed quantized gradient approach, which is characterized by adaptive communications of the quantized gradients. The key idea to save communications from the worker to the server is to quantize gradients as well as skip less informative quantized gradient communications by reusing previous gradients. Quantizing and skipping result in ‘lazy’ worker-server communications, which justifies the term Lazily Aggregated Quantized (LAQ) gradient. Theoretically, the LAQ algorithm achieves the same linear convergence as the gradient descent in the strongly convex case, while effecting major savings in the communication in terms of transmitted bits and communication rounds . 本文围绕通信高效的联邦学习问题，发展了一种新的分布式量化梯度方法，其特点是量化梯度的自适应通信。保存工作者到服务器之间的通信的关键**是量化梯度，并通过重用先前的梯度跳过信息量较少的量化梯度通信。量化和跳过会导致"懒惰"的工作者-服务器通信，这就证明了Lazily Aggregate Quantized (LAQ)梯度一词的合理性。理论上，LAQ算法在强凸的情况下实现了与梯度下降相同的线性收敛，同时在传输比特数和通信轮数方面大大节省了通信开销。 ↩
A novel methodology coined FedPop by recasting personalised FL into the population modeling paradigm where clients' models involve fixed common population parameters and random individual ones, aiming at explaining data heterogeneity. To derive convergence guarantees for our scheme, we introduce a new class of federated stochastic optimisation algorithms which relies on Markov chain Monte Carlo methods. Compared to existing personalised FL methods, the proposed methodology has important benefits: it is robust to client drift, practical for inference on new clients, and above all, enables uncertainty quantification under mild computational and memory overheads. We provide non-asymptotic convergence guarantees for the proposed algorithms. 一种新的方法被称为FedPop，它将个性化的FL重塑为群体建模范式，客户的模型涉及固定的共同群体参数和随机的个体参数，旨在解释数据的异质性。为了得出我们方案的收敛保证，我们引入了一类新的联邦随机优化算法，该算法依赖于马尔科夫链蒙特卡洛方法。与现有的个性化FL方法相比，所提出的方法具有重要的优势：它对客户的漂移是稳健的，对新客户的推断是实用的，最重要的是，在温和的计算和内存开销下，可以进行不确定性量化。我们为提议的算法提供了非渐进收敛保证。 ↩
We aim to formally represent this problem and address these fairness issues using concepts from co-operative game theory and social choice theory. We model the task of learning a shared predictor in the federated setting as a fair public decision making problem, and then define the notion of core-stable fairness: Given N agents, there is no subset of agents S that can benefit significantly by forming a coalition among themselves based on their utilities UN and US. Core-stable predictors are robust to low quality local data from some agents, and additionally they satisfy Proportionality (each agent gets at least 1/n fraction of the best utility that she can get from any predictor) and Pareto-optimality (there exists no model that can increase the utility of an agent without decreasing the utility of another), two well sought-after fairness and efficiency notions within social choice. We then propose an efficient federated learning protocol CoreFed to optimize a core stable predictor. CoreFed determines a core-stable predictor when the loss functions of the agents are convex. CoreFed also determines approximate core-stable predictors when the loss functions are not convex, like mooth neural networks. We further show the existence of core-stable predictors in more general settings using Kakutani's fixed point theorema. 我们旨在利用合作博弈理论和社会选择理论的概念来正式表示这个问题并解决这些公平性问题。我们把在联盟环境中学习共享预测器的任务建模为一个公平的公共决策问题，然后定义核心稳定的公平概念。给定N个代理人，没有一个代理人的子集S可以通过在他们之间形成一个基于他们的效用UN和US的联盟而显著受益。核心稳定的预测器对一些代理人的低质量本地数据具有鲁棒性，此外，它们还满足Proportionality（每个代理人从任何预测器中得到的最佳效用的至少1/n部分）和Pareto-optimality（不存在任何模型可以在增加一个代理人的效用的同时不减少另一个代理人的效用），这是社会选择中两个广受欢迎的公平和效率概念。然后，我们提出了一个高效的联邦学习协议CoreFed来优化一个核心稳定的预测器。当代理人的损失函数是凸的时候，CoreFed确定了一个核心稳定的预测器。当损失函数不是凸的时候，CoreFed也能确定近似的核心稳定预测器，比如摩斯神经网络。我们利用Kakutani的固定点定理，进一步证明了在更一般的情况下核心稳定预测器的存在。 ↩
The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature unidimensional transformation often used to Gaussianize features in machine learning. In this paper, we investigate the problem of applying the YJ transformation in a cross-silo Federated Learning setting under privacy constraints. For the first time, we prove that the YJ negative log-likelihood is in fact convex, which allows us to optimize it with exponential search. We numerically show that the resulting algorithm is more stable than the state-of-the-art approach based on the Brent minimization method. Building on this simple algorithm and Secure Multiparty Computation routines, we propose SECUREFEDYJ, a federated algorithm that performs a pooled-equivalent YJ transformation without leaking more information than the final fitted parameters do. Quantitative experiments on real data demonstrate that, in addition to being secure, our approach reliably normalizes features across silos as well as if data were pooled, making it a viable approach for safe federated feature Gaussianization. Yeo-Johnson（YJ）变换是一个标准的参数化的每特征单维变换，通常用于机器学习的高斯化特征。在本文中，我们研究了在隐私约束下，在跨语境的联邦学习环境中应用YJ转换的问题。我们首次证明了YJ负对数可能性实际上是凸的，这使我们能够用指数搜索来优化它。我们在数值上表明，所得到的算法比基于布伦特最小化方法的最先进的方法更稳定。在这个简单的算法和安全多方计算程序的基础上，我们提出了SECUREFEDYJ，这是一个联邦算法，在不泄露比最终拟合参数更多信息的情况下执行集合等效的YJ转换。在真实数据上的定量实验表明，除了安全之外，我们的方法还能可靠地将不同筒仓的特征归一化，就像数据被汇集起来一样，这使得它成为安全联邦特征高斯化的可行方法。 ↩
A simple yet effective model-heterogeneous FL method named FedRolex to tackle this constraint. Unlike the model-homogeneous scenario, the fundamental challenge of model heterogeneity in FL is that different parameters of the global model are trained on heterogeneous data distributions. FedRolex addresses this challenge by rolling the submodel in each federated iteration so that the parameters of the global model are evenly trained on the global data distribution across all devices, making it more akin to model-homogeneous training. 一个名为FedRolex的简单而有效的模型-异质性FL方法来解决这一约束。与模型同质化的情况不同，FL中模型异质化的根本挑战是全局模型的不同参数是在异质的数据分布上训练的。FedRolex通过在每个联邦迭代中滚动子模型来解决这个挑战，这样全局模型的参数就会在所有设备的全局数据分布上均匀地训练，使其更类似于模型同质化训练。 ↩
The data-owning clients may drop out of the training process arbitrarily. These characteristics will significantly degrade the training performance. This paper proposes a Dropout-Resilient Secure Federated Learning (DReS-FL) framework based on Lagrange coded computing (LCC) to tackle both the non-IID and dropout problems. The key idea is to utilize Lagrange coding to secretly share the private datasets among clients so that the effects of non-IID distribution and client dropouts can be compensated during local gradient computations. To provide a strict privacy guarantee for local datasets and correctly decode the gradient at the server, the gradient has to be a polynomial function in a finite field, and thus we construct polynomial integer neural networks (PINNs) to enable our framework. Theoretical analysis shows that DReS-FL is resilient to client dropouts and provides privacy protection for the local datasets. 拥有数据的客户可能会任意退出训练过程。这些特点将大大降低训练性能。本文提出了一个基于拉格朗日编码计算（LCC）的辍学弹性安全联邦学习（DReS-FL）框架来解决非IID和辍学问题。其关键**是利用拉格朗日编码在客户之间秘密分享私人数据集，以便在本地梯度计算中补偿非IID分布和客户退出的影响。为了给本地数据集提供严格的隐私保证并在服务器上正确解码梯度，梯度必须是有限域中的多项式函数，因此我们构建了多项式整数神经网络（PINNs）来实现我们的框架。理论分析表明，DReS-FL对客户端辍学有弹性，并为本地数据集提供隐私保护。 ↩
Since in real-world applications the data may contain bias on fairness-sensitive features (e.g., gender), VFL models may inherit bias from training data and become unfair for some user groups. However, existing fair machine learning methods usually rely on the centralized storage of fairness-sensitive features to achieve model fairness, which are usually inapplicable in federated scenarios. In this paper, we propose a fair vertical federated learning framework (FairVFL), which can improve the fairness of VFL models. The core idea of FairVFL is to learn unified and fair representations of samples based on the decentralized feature fields in a privacy-preserving way. Specifically, each platform with fairness-insensitive features first learns local data representations from local features. Then, these local representations are uploaded to a server and aggregated into a unified representation for the target task. In order to learn a fair unified representation, we send it to each platform storing fairness-sensitive features and apply adversarial learning to remove bias from the unified representation inherited from the biased data. Moreover, for protecting user privacy, we further propose a contrastive adversarial learning method to remove private information from the unified representation in server before sending it to the platforms keeping fairness-sensitive features. 由于在现实世界的应用中，数据可能包含对公平性敏感的特征（如性别）的偏见，VFL模型可能会从训练数据中继承偏见，并对一些用户群体变得不公平。然而，现有的公平机器学习方法通常依赖于公平性敏感特征的集中存储来实现模型的公平性，这在联盟场景中通常是不适用的。在本文中，我们提出了一个公平的垂直联邦学习框架（FairVFL），它可以提高VFL模型的公平性。FairVFL的核心**是以保护隐私的方式，基于分散的特征场学习统一的、公平的样本表示。具体来说，每个具有公平性不敏感特征的平台首先从本地特征中学习本地数据表示。然后，这些本地表征被上传到服务器上，并聚合成目标任务的一个统一表征。为了学习一个公平的统一表征，我们将其发送到每个存储公平性敏感特征的平台，并应用对抗性学习来消除从有偏见的数据中继承的统一表征的偏见。此外，为了保护用户的隐私，我们进一步提出了一种对比性的对抗性学习方法，在将统一表示发送到保存公平性敏感特征的平台之前，从服务器中去除私人信息。 ↩
We study distributed optimization methods based on the local training (LT) paradigm, i.e., methods which achieve communication efficiency by performing richer local gradient-based training on the clients before (expensive) parameter averaging is allowed to take place. While these methods were first proposed about a decade ago, and form the algorithmic backbone of federated learning, there is an enormous gap between their practical performance, and our theoretical understanding. Looking back at the progress of the field, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation was initiated by the ProxSkip method of Mishchenko et al. (2022), whose analysis provided the first theoretical confirmation that LT is a communication acceleration mechanism. Inspired by this recent progress, we contribute to the 5th generation of LT methods by showing that it is possible to enhance ProxSkip further using variance reduction. While all previous theoretical results for LT methods ignore the cost of local work altogether, and are framed purely in terms of the number of communication rounds, we construct a method that can be substantially faster in terms of the total training time than the state-of-the-art method ProxSkip in theory and practice in the regime when local computation is sufficiently expensive. We characterize this threshold theoretically, and confirm our theoretical predictions with empirical results. Our treatment of variance reduction is generic, and can work with a large number of variance reduction techniques, which may lead to future applications in the future. 我们研究了基于局部训练（LT）范式的分布式优化方法，即在允许进行（昂贵的）参数平均化之前，通过在客户端进行更丰富的基于局部梯度的训练来实现通信效率。虽然这些方法是在大约十年前首次提出的，并且形成了联邦学习的算法支柱，但是在它们的实际性能和我们的理论理解之间存在着巨大的差距。回顾该领域的进展，我们确定了5代LT方法：1）启发式，2）同质式，3）亚线性，4）线性，以及5）加速式。第5代是由Mishchenko等人（2022）的ProxSkip方法发起的，其分析首次从理论上证实了LT是一种通信加速机制。受这一最新进展的启发，我们为第5代LT方法做出了贡献，表明有可能利用方差减少来进一步增强ProxSkip。虽然之前所有关于LT方法的理论结果都完全忽略了局部工作的成本，而仅仅是以通信轮数为框架，但我们构建了一种方法，在理论和实践中，当局部计算足够昂贵时，其总训练时间可以比最先进的方法ProxSkip快很多。我们从理论上描述了这个阈值，并通过经验结果证实了我们的理论预测。我们对方差减少的处理是通用的，可以与大量的方差减少技术一起工作，这可能导致未来的应用。 ↩
Vertical Federated Learning (VFL) methods are facing two challenges: (1) scalability when # participants grows to even modest scale and (2) diminishing return w.r.t. # participants: not all participants are equally important and many will not introduce quality improvement in a large consortium. Inspired by these two challenges, in this paper, we ask: How can we select l out of m participants, where l≪m , that are most important?We call this problem Vertically Federated Participant Selection, and model it with a principled mutual information-based view. Our first technical contribution is VF-MINE---a Vertically Federated Mutual INformation Estimator---that uses one of the most celebrated algorithms in database theory---Fagin's algorithm as a building block. Our second contribution is to further optimize VF-MINE to enable VF-PS, a group testing-based participant selection framework. 垂直联邦学习（VFL）方法面临着两个挑战：（1）当参与者数量增长到一定规模时的可扩展性；（2）对参与者的回报递减：不是所有的参与者都同样重要，许多参与者不会在一个大型联盟中引入质量改进。受这两个挑战的启发，在本文中，我们问：我们如何从m个参与者中选择l个，其中l≪m，是最重要的。我们称这个问题为垂直联邦参与者选择，并以基于相互信息的原则性观点为其建模。我们的第一个技术贡献是VF-MINE--一个垂直联邦的相互信息估计器--它使用数据库理论中最著名的算法之一--Fagin的算法作为构建模块。我们的第二个贡献是进一步优化VF-MINE，以实现VF-PS，一个基于小组测试的参与者选择框架。 ↩
A novel two-stage Data-free One-Shot Federated Learning(DENSE) framework, which trains the global model by a data generation stage and a model distillation stage. DENSE is a practical one-shot FL method that can be applied in reality due to the following advantages:(1) DENSE requires no additional information compared with other methods (except the model parameters) to be transferred between clients and the server;(2) DENSE does not require any auxiliary dataset for training;(3) DENSE considers model heterogeneity in FL, i.e. different clients can have different model architectures. 一种新颖的两阶段无数据单次联邦学习（DENSE）框架，它通过数据生成阶段和模型提炼阶段来训练全局模型。DENSE是一种实用的一次性FL方法，由于以下优点可以在现实中应用：（1）与其他方法相比，DENSE不需要在客户端和服务器之间传输额外的信息（除了模型参数）；（2）DENSE不需要任何辅助数据集进行训练；（3）DENSE考虑了FL中的模型异质性，即不同客户端可以有不同的模型架构。 ↩
We study the problem of FAT(federated adversarial training) under label skewness, and firstly reveal one root cause of the training instability and natural accuracy degradation issues: skewed labels lead to non-identical class probabilities and heterogeneous local models. We then propose a Calibrated FAT (CalFAT) approach to tackle the instability issue by calibrating the logits adaptively to balance the classes. 我们研究了标签偏斜下的FAT（联邦对抗训练）问题，首先揭示了训练不稳定和自然准确率下降问题的一个根本原因：偏斜的标签导致了非相同的类概率和异质的局部模型。然后，我们提出了一种校准的FAT（CalFAT）方法，通过自适应地校准对数来平衡类，来解决不稳定问题。 ↩
Federated min-max learning has received increasing attention in recent years thanks to its wide range of applications in various learning paradigms. We propose a new algorithmic framework called stochastic sampling averaging gradient descent ascent (SAGDA), which i) assembles stochastic gradient estimators from randomly sampled clients as control variates and ii) leverages two learning rates on both server and client sides. We show that SAGDA achieves a linear speedup in terms of both the number of clients and local update steps, which yields an O(ϵ−2) communication complexity that is orders of magnitude lower than the state of the art. Interestingly, by noting that the standard federated stochastic gradient descent ascent (FSGDA) is in fact a control-variate-free special version of SAGDA, we immediately arrive at an O(ϵ−2) communication complexity result for FSGDA. Therefore, through the lens of SAGDA, we also advance the current understanding on communication complexity of the standard FSGDA method for federated min-max learning. 近年来，由于其在各种学习范式中的广泛应用，联邦最小-最大学习得到了越来越多的关注。我们提出了一个新的算法框架，称为随机抽样平均梯度下降上升法（SAGDA），它i）从随机抽样的客户端组装随机梯度估计器作为控制变量，ii）在服务器和客户端利用两个学习速率。我们表明，SAGDA在客户数量和局部更新步骤方面都实现了线性加速，这产生了O(ϵ-2)的通信复杂度，比目前的技术水平要低几个数量级。有趣的是，通过注意到标准联邦随机梯度下降法（FSGDA）实际上是SAGDA的无控制变量的特殊版本，我们立即得出了FSGDA的O(ϵ-2)通信复杂度结果。因此，通过SAGDA的视角，我们也推进了目前对标准FSGDA方法的通信复杂度的理解，以实现联邦的最小最大学习。 ↩
A key assumption in most existing works on FL algorithms' convergence analysis is that the noise in stochastic first-order information has a finite variance. Although this assumption covers all light-tailed (i.e., sub-exponential) and some heavy-tailed noise distributions (e.g., log-normal, Weibull, and some Pareto distributions), it fails for many fat-tailed noise distributions (i.e., heavier-tailed'' with potentially infinite variance) that have been empirically observed in the FL literature. To date, it remains unclear whether one can design convergent algorithms for FL systems that experience fat-tailed noise. This motivates us to fill this gap in this paper by proposing an algorithmic framework called FAT-Clipping (federated averaging with two-sided learning rates and clipping), which contains two variants: FAT-Clipping per-round (FAT-Clipping-PR) and FAT-Clipping per-iteration (FAT-Clipping-PI). 在大多数现有的关于FL算法收敛性分析的工作中，一个关键的假设是随机一阶信息中的噪声具有有限的方差。尽管这一假设涵盖了所有轻尾（即亚指数）和一些重尾噪声分布（如对数正态分布、Weibull分布和一些Pareto分布），但对于FL文献中实证观察到的许多肥尾噪声分布（即可能具有无限方差的重尾''）来说，它是失败的。到目前为止，我们还不清楚是否可以为经历肥尾噪声的FL系统设计收敛算法。这促使我们在本文中提出了一个名为FAT-Clipping（具有双面学习率和剪切的联邦平均法）的算法框架来填补这一空白，该框架包含两个变体。FAT-Clipping per-round（FAT-Clipping-PR）和FAT-Clipping per-iteration（FAT-Clipping-PI）。 ↩
FedSubAvg, We study federated learning from the new perspective of feature heat, where distinct data features normally involve different numbers of clients, generating the differentiation of hot and cold features. Meanwhile, each client’s local data tend to interact with part of features, updating only the feature-related part of the full model, called a submodel. We further identify that the classical federated averaging algorithm (FedAvg) or its variants, which randomly selects clients to participate and uniformly averages their submodel updates, will be severely slowed down, because different parameters of the global model are optimized at different speeds. More specifically, the model parameters related to hot (resp., cold) features will be updated quickly (resp., slowly). We thus propose federated submodel averaging (FedSubAvg), which introduces the number of feature-related clients as the metric of feature heat to correct the aggregation of submodel updates. We prove that due to the dispersion of feature heat, the global objective is ill-conditioned, and FedSubAvg works as a suitable diagonal preconditioner. We also rigorously analyze FedSubAvg’s convergence rate to stationary points. 我们从特征热的新角度来研究联邦学习，不同的数据特征通常涉及不同数量的客户端，产生了冷热特征的区分。同时，每个客户的本地数据往往与部分特征交互，只更新完整模型中与特征相关的部分，称为子模型。我们进一步确定，经典的联邦平均算法（FedAvg）或其变体，即随机选择客户参与并统一平均他们的子模型更新，将被严重减慢，因为全局模型的不同参数是以不同的速度优化。更具体地说，与热（或冷）特征相关的模型参数将被快速（或缓慢）更新。因此，我们提出了联邦子模型平均法（FedSubAvg），它引入了与特征相关的客户数量作为特征热度的度量，以修正子模型更新的聚合。我们证明，由于特征热度的分散，全局目标是无条件的，而FedSubAvg作为一个合适的对角线先决条件发挥作用。我们还严格分析了FedSubAvg对静止点的收敛率。 ↩
BooNTK, State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions. We show that this performance disparity can largely be attributed to optimization challenges presented by nonconvexity. Specifically, we find that the early layers of the network do learn useful features, but the final layers fail to make use of them. That is, federated optimization applied to this non-convex problem distorts the learning of the final layers. Leveraging this observation, we propose a Train-Convexify-Train (TCT) procedure to sidestep this issue: first, learn features using off-the-shelf methods (e.g., FedAvg); then, optimize a convexified problem obtained from the network's empirical neural tangent kernel approximation. 当客户具有不同的数据分布时，最先进的联邦学习方法的表现会比集中式的对应方法差很多。我们表明，这种性能差异主要归因于非凸性带来的优化挑战。具体来说，我们发现网络的早期层确实学到了有用的特征，但最后一层却无法利用它们。也就是说，应用于这个非凸问题的联邦优化扭曲了最终层的学习。利用这一观察，我们提出了一个Train-Convexify-Train（TCT）程序来回避这一问题：首先，使用现成的方法（如FedAvg）学习特征；然后，优化一个从网络的经验神经切线核近似中得到的凸化问题。 ↩
SoteriaFL, A unified framework that enhances the communication efficiency of private federated learning with communication compression. Exploiting both general compression operators and local differential privacy, we first examine a simple algorithm that applies compression directly to differentially-private stochastic gradient descent, and identify its limitations. We then propose a unified framework SoteriaFL for private federated learning, which accommodates a general family of local gradient estimators including popular stochastic variance-reduced gradient methods and the state-of-the-art shifted compression scheme. 具有通信压缩的增强私有联邦学习通信效率的统一框架。利用一般的压缩算子和局部差分隐私，我们首先研究了一种简单的直接将压缩应用于差分隐私随机梯度下降的算法，并指出其局限性。然后，我们提出了一个用于私有联邦学习的统一框架SoteriaFL，它包含了一个通用的局部梯度估计器家族，包括流行的随机方差减少梯度方法和最先进的移位压缩方案。 ↩
FILM, A novel attack method FILM (Federated Inversion attack for Language Models) for federated learning of language models---for the first time, we show the feasibility of recovering text from large batch sizes of up to 128 sentences. Different from image-recovery methods which are optimized to match gradients, we take a distinct approach that first identifies a set of words from gradients and then directly reconstructs sentences based on beam search and a prior-based reordering strategy. The key insight of our attack is to leverage either prior knowledge in pre-trained language models or memorization during training. Despite its simplicity, we demonstrate that FILM can work well with several large-scale datasets---it can extract single sentences with high fidelity even for large batch sizes and recover multiple sentences from the batch successfully if the attack is applied iteratively. 一种新颖的针对语言模型联邦学习的攻击方法FILM (针对语言模型的联邦反演攻击) - -首次展示了从多达128个句子的大批量文本中恢复文本的可行性。与为匹配梯度而优化的图像恢复方法不同，我们采取了一种独特的方法，首先从梯度中识别一组单词，然后根据光束搜索和基于先验的重新排序策略直接重建句子。我们攻击的关键见解是在预训练的语言模型中利用先验知识，或者在训练过程中进行记忆。尽管FILM简单，但我们证明了它可以在几个大规模数据集上很好地工作- -即使对于大批量的数据集，它也可以高保真地提取单个句子，如果迭代地应用攻击，它可以成功地从批处理中恢复多个句子。 ↩
FedPCL, A lightweight framework where clients jointly learn to fuse the representations generated by multiple fixed pre-trained models rather than training a large-scale model from scratch. This leads us to a more practical FL problem by considering how to capture more client-specific and class-relevant information from the pre-trained models and jointly improve each client's ability to exploit those off-the-shelf models. Here, we design a Federated Prototype-wise Contrastive Learning (FedPCL) approach which shares knowledge across clients through their class prototypes and builds client-specific representations in a prototype-wise contrastive manner. Sharing prototypes rather than learnable model parameters allows each client to fuse the representations in a personalized way while keeping the shared knowledge in a compact form for efficient communication. 一个轻量级的框架，客户共同学习融合多个固定的预训练模型所产生的表征，而不是从头开始训练一个大规模的模型。这将我们引向一个更实际的FL问题，即考虑如何从预训练的模型中获取更多特定于客户和与类相关的信息，并共同提高每个客户利用这些现成的模型的能力。在这里，我们设计了一个联邦原型对比学习（FedPCL）的方法，通过客户的类别原型在客户之间分享知识，并以原型对比的方式建立客户的特定表征。分享原型而不是可学习的模型参数允许每个客户以个性化的方式融合表征，同时将共享的知识保持在一个紧凑的形式，以便有效沟通。 ↩
To achieve resource-adaptive federated learning, we introduce a simple yet effective mechanism, termed All-In-One Neural Composition, to systematically support training complexity-adjustable models with flexible resource adaption. It is able to efficiently construct models at various complexities using one unified neural basis shared among clients, instead of pruning the global model into local ones. The proposed mechanism endows the system with unhindered access to the full range of knowledge scattered across clients and generalizes existing pruning-based solutions by allowing soft and learnable extraction of low footprint models. 为了实现资源自适应的联邦学习，我们引入了一种简单而有效的机制，称为"一体式神经合成"，以系统支持具有灵活资源自适应的训练复杂度可调模型。它能够使用客户机之间共享的一个统一神经基础在各种复杂情况下高效地构建模型，而不是将全局模型剪枝为局部模型。所提出的机制使系统能够不受阻碍地访问分散在客户端的所有知识，并通过允许对低足迹模型进行软和可学习的提取来推广现有的基于剪枝的解决方案。 ↩
Inspired by Bayesian hierarchical models, we develop a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients' training. Such a balance is derived from the inter-client and intra-client uncertainty quantification. A larger inter-client variation implies more personalization is needed. Correspondingly, our method uses uncertainty-driven local training steps an aggregation rule instead of conventional local fine-tuning and sample size-based aggregation. 受贝叶斯层次模型的启发，我们开发了一种自感知的个性化FL方法，每个客户端可以自动平衡其本地个人模型和隐式贡献于其他客户端训练的全局模型的训练。这种平衡来自于客户端间和客户端内的不确定性量化。更大的客户间差异意味着更多的个性化需求。相应地，我们的方法使用不确定性驱动的局部训练步骤作为聚合规则，而不是传统的局部微调和基于样本量的聚合。 ↩
In this paper, we study a large-scale multi-agent minimax optimization problem, which models many interesting applications in statistical learning and game theory, including Generative Adversarial Networks (GANs). The overall objective is a sum of agents' private local objective functions. We first analyze an important special case, empirical minimax problem, where the overall objective approximates a true population minimax risk by statistical samples. We provide generalization bounds for learning with this objective through Rademacher complexity analysis. Then, we focus on the federated setting, where agents can perform local computation and communicate with a central server. Most existing federated minimax algorithms either require communication per iteration or lack performance guarantees with the exception of Local Stochastic Gradient Descent Ascent (SGDA), a multiple-local-update descent ascent algorithm which guarantees convergence under a diminishing stepsize. By analyzing Local SGDA under the ideal condition of no gradient noise, we show that generally it cannot guarantee exact convergence with constant stepsizes and thus suffers from slow rates of convergence. To tackle this issue, we propose FedGDA-GT, an improved Federated (Fed) Gradient Descent Ascent (GDA) method based on Gradient Tracking (GT). When local objectives are Lipschitz smooth and strongly-convex-strongly-concave, we prove that FedGDA-GT converges linearly with a constant stepsize to global ϵ-approximation solution with O(log(1/ϵ)) rounds of communication, which matches the time complexity of centralized GDA method. Finally, we numerically show that FedGDA-GT outperforms Local SGDA. 在本文中，我们研究了一个大规模的多代理最小优化问题，它模拟了统计学习和博弈论中许多有趣的应用，包括生成对抗网络（GANs）。总体目标是代理人的私有局部目标函数的总和。我们首先分析了一个重要的特例，即经验最小值问题，其中总体目标是通过统计样本逼近真实的群体最小值风险。我们通过Rademacher复杂度分析，为这个目标的学习提供泛化界线。然后，我们专注于联盟环境，其中代理可以执行本地计算并与**服务器通信。大多数现有的联邦最小化算法要么需要每次迭代都进行通信，要么缺乏性能保证，但本地随机梯度上升算法（SGDA）除外，它是一种多本地更新的下降上升算法，保证在步长减小的情况下收敛。通过在没有梯度噪声的理想条件下分析Local SGDA，我们发现一般来说它不能保证在恒定的步长下准确收敛，因此存在收敛速度慢的问题。为了解决这个问题，我们提出了FedGDA-GT，一种基于梯度跟踪（GT）的改进的联邦（Fed）梯度下降上升（GDA）方法。当局部目标是Lipschitz平滑和强凸-强凹时，我们证明FedGDA-GT以恒定的步长线性收敛到全局的ϵ近似解，只需O(log(1/ϵ)) 轮通信，这与集中式GDA方法的时间复杂度相符。最后，我们用数字表明，FedGDA-GT优于Local SGDA。 ↩
SemiFL to address the problem of combining communication efficient FL like FedAvg with Semi-Supervised Learning (SSL). In SemiFL, clients have completely unlabeled data and can train multiple local epochs to reduce communication costs, while the server has a small amount of labeled data. We provide a theoretical understanding of the success of data augmentation-based SSL methods to illustrate the bottleneck of a vanilla combination of communication efficient FL with SSL. To address this issue, we propose alternate training to 'fine-tune global model with labeled data' and 'generate pseudo-labels with global model.' SemiFL是为了解决像FedAvg这样的通信效率高的FL与半监督学习（SSL）相结合的问题。在SemiFL中，客户拥有完全未标记的数据，并且可以训练多个本地历时以减少通信成本，而服务器拥有少量的标记数据。我们对基于数据增强的SSL方法的成功提供了一个理论上的理解，以说明通信效率高的FL与SSL的虚构组合的瓶颈。为了解决这个问题，我们提出了 "用标签数据微调全局模型 "和 "用全局模型生成伪标签 "的替代训练。 ↩
This study starts from an analogy to continual learning and suggests that forgetting could be the bottleneck of federated learning. We observe that the global model forgets the knowledge from previous rounds, and the local training induces forgetting the knowledge outside of the local distribution. Based on our findings, we hypothesize that tackling down forgetting will relieve the data heterogeneity problem. To this end, we propose a novel and effective algorithm, Federated Not-True Distillation (FedNTD), which preserves the global perspective on locally available data only for the not-true classes. 这项研究从持续学习的类比开始，表明遗忘可能是联邦学习的瓶颈。我们观察到全局模型忘记了前几轮的知识，而本地训练会导致忘记本地分布之外的知识。基于我们的发现，我们假设处理遗忘会缓解数据异质性问题。为此，我们提出了一种新颖而有效的算法- -联邦非真实蒸馏( FedNTD )，它仅对非真实类保留本地可用数据的全局视角。 ↩
We propose a simple yet novel representation learning framework, namely FedSR, which enables domain generalization while still respecting the decentralized and privacy-preserving natures of this FL setting. Motivated by classical machine learning algorithms, we aim to learn a simple representation of the data for better generalization. In particular, we enforce an L2-norm regularizer on the representation and a conditional mutual information (between the representation and the data given the label) regularizer to encourage the model to only learn essential information (while ignoring spurious correlations such as the background). Furthermore, we provide theoretical connections between the above two objectives and representation alignment in domain generalization. 我们提出了一个简单但新颖的表示学习框架，即FedSR，它允许领域泛化，同时仍然尊重这种FL设置的去中心化和隐私保护性质。受经典机器学习算法的启发，我们旨在学习数据的简单表示以获得更好的泛化能力。特别地，我们在表示上强制一个L2范数正则化器和一个条件互信息(在给定标签的表示和数据之间)正则化器，以鼓励模型只学习基本信息(而忽略虚假的相关性,如背景)。此外，我们提供了上述两个目标与领域泛化中的表示对齐之间的理论联系。 ↩
In real-world federated learning scenarios, participants could have their own personalized labels which are incompatible with those from other clients, due to using different label permutations or tackling completely different tasks or domains. However, most existing FL approaches cannot effectively tackle such extremely heterogeneous scenarios since they often assume that (1) all participants use a synchronized set of labels, and (2) they train on the same tasks from the same domain. In this work, to tackle these challenges, we introduce Factorized-FL, which allows to effectively tackle label- and task-heterogeneous federated learning settings by factorizing the model parameters into a pair of rank-1 vectors, where one captures the common knowledge across different labels and tasks and the other captures knowledge specific to the task for each local model. Moreover, based on the distance in the client-specific vector space, Factorized-FL performs selective aggregation scheme to utilize only the knowledge from the relevant participants for each client. 在现实世界的联邦学习场景中，由于使用不同的标签组合或处理完全不同的任务或领域，参与者可能有自己的个性化标签，而这些标签与其他客户的标签不兼容。然而，大多数现有的FL方法不能有效地处理这种极端异质的场景，因为它们通常假设（1）所有参与者使用同步的标签集，以及（2）他们在同一领域的相同任务上训练。在这项工作中，为了应对这些挑战，我们引入了Factorized-FL，它可以通过将模型参数分解为一对等级1的向量来有效地解决标签和任务异质的联邦学习环境，其中一个捕捉不同标签和任务的共同知识，另一个捕捉每个本地模型的特定任务知识。此外，根据客户特定向量空间中的距离，Factorized-FL执行选择性聚合方案，只利用每个客户的相关参与者的知识。 ↩
We study federated contextual linear bandits, where M agents cooperate with each other to solve a global contextual linear bandit problem with the help of a central server. We consider the asynchronous setting, where all agents work independently and the communication between one agent and the server will not trigger other agents' communication. We propose a simple algorithm named FedLinUCB based on the principle of optimism. We prove that the regret of FedLinUCB is bounded by ˜O(d√∑Mm=1Tm) and the communication complexity is ˜O(dM2), where d is the dimension of the contextual vector and Tm is the total number of interactions with the environment by agent m. To the best of our knowledge, this is the first provably efficient algorithm that allows fully asynchronous communication for federated linear bandits, while achieving the same regret guarantee as in the single-agent setting.我们研究联邦式的上下文线性匪徒问题，其中M个代理相互协作，借助中心服务器解决一个全局的上下文线性匪徒问题。我们考虑异步设置，其中所有代理独立工作，并且一个代理与服务器之间的通信不会触发其他代理的通信。我们基于乐观原则提出了一个简单的算法FedLinUCB。我们证明了FedLinUCB的后悔度以˜O(d√∑Mm=1Tm)为界，通信复杂度为˜O(dM2)，其中d是上下文向量的维数，Tm是代理m与环境交互的总数。据我们所知，这是第一个可证明有效的算法，允许联邦线性匪徒完全异步通信，同时实现与单代理设置中相同的遗憾保证。 ↩
Vertical federated learning (VFL), where parties share the same set of samples but only hold partial features, has a wide range of real-world applications. However, most existing studies in VFL disregard the record linkage” process. They design algorithms either assuming the data from different parties can be exactly linked or simply linking each record with its most similar neighboring record. These approaches may fail to capture the key features from other less similar records. Moreover, such improper linkage cannot be corrected by training since existing approaches provide no feedback on linkage during training. In this paper, we design a novel coupled training paradigm, FedSim, that integrates one-to-many linkage into the training process. Besides enabling VFL in many real-world applications with fuzzy identifiers, FedSim also achieves better performance in traditional VFL tasks. Moreover, we theoretically analyze the additional privacy risk incurred by sharing similarities. 纵向联邦学习(VFL)，其中各方共享相同的样本集，但只保留部分特征，它有广泛的实际应用。然而，VFL中的大多数现有研究忽略了记录链接过程。他们设计算法，要么假设来自不同方的数据可以完全链接，要么简单地将每个记录与其最相似的相邻记录链接起来。这些方法可能无法从其他不太相似的记录中捕获关键特征。而且，这种不恰当的联结不能通过训练来纠正，因为现有方法在训练过程中没有提供关于联结的反馈。在本文中，我们设计了一种新的耦合训练范式FedSim，它将一对多连接集成到训练过程中。除了在许多具有模糊标识符的实际应用程序中启用VFL之外，FedSim还在传统的VFL任务中实现了更好的性能。此外，我们从理论上分析了共享相似性所带来的额外隐私风险。 ↩
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FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research. FedScale是一个联邦学习（FL）基准测试套件，具有现实的数据集和可扩展的运行时间，以实现可重复的FL研究。 ↩ ↩²
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We exploit the potentials of heterogeneous model settings and propose a novel training framework to employ personalized models for different clients. Specifically, we formulate the aggregation procedure in original pFL into a personalized group knowledge transfer training algorithm, namely, KT-pFL, which enables each client to maintain a personalized soft prediction at the server side to guide the others' local training. KT-pFL updates the personalized soft prediction of each client by a linear combination of all local soft predictions using a knowledge coefficient matrix, which can adaptively reinforce the collaboration among clients who own similar data distribution. Furthermore, to quantify the contributions of each client to others' personalized training, the knowledge coefficient matrix is parameterized so that it can be trained simultaneously with the models. The knowledge coefficient matrix and the model parameters are alternatively updated in each round following the gradient descent way. 我们利用异质模型设置的潜力，提出了一个新的训练框架，为不同的客户采用个性化的模型。具体来说，我们将原始pFL中的聚合程序制定为一种个性化的群体知识转移训练算法，即KT-pFL，它使每个客户在服务器端保持一个个性化的软预测，以指导其他人的本地训练。KT-pFL通过使用知识系数矩阵对所有本地软预测进行线性组合来更新每个客户端的个性化软预测，这可以自适应地加强拥有相似数据分布的客户端之间的协作。此外，为了量化每个客户对其他客户的个性化训练的贡献，知识系数矩阵被参数化，以便它可以与模型同时训练。知识系数矩阵和模型参数在每一轮中按照梯度下降的方式交替更新。 ↩
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CE propose the concept of benefit graph which describes how each client can benefit from collaborating with other clients and advance a Pareto optimization approach to identify the optimal collaborators. CE提出了利益图的概念，描述了每个客户如何从与其他客户的合作中获益，并提出了帕累托优化方法来确定最佳合作者。 ↩
SuPerFed, a personalized federated learning method that induces an explicit connection between the optima of the local and the federated model in weight space for boosting each other. SuPerFed，一种个性化联邦学习方法，该方法在本地模型和联邦模型的权重空间中诱导出一个明确的连接，以促进彼此的发展。 ↩
FedMSplit framework, which allows federated training over multimodal distributed data without assuming similar active sensors in all clients. The key idea is to employ a dynamic and multi-view graph structure to adaptively capture the correlations amongst multimodal client models. FedMSplit框架，该框架允许在多模态分布式数据上进行联邦训练，而不需要假设所有客户端都有类似的主动传感器。其关键**是采用动态和多视图图结构来适应性地捕捉多模态客户模型之间的相关性。 ↩
Comm-FedBiO propose a learning-based reweighting approach to mitigate the effect of noisy labels in FL. Comm-FedBiO提出了一种基于学习的重加权方法，以减轻FL中噪声标签的影响。 ↩
FLDetector detects malicious clients via checking their model-updates consistency to defend against model poisoning attacks with a large number of malicious clients. FLDetector 通过检查其模型更新的一致性来检测恶意客户，以防御大量恶意客户的模型中毒攻击。 ↩
FedSVD, a practical lossless federated SVD method over billion-scale data, which can simultaneously achieve lossless accuracy and high efficiency. FedSVD，是一种实用的亿级数据上的无损联邦SVD方法，可以同时实现无损精度和高效率。 ↩
Federated Learning-to-Dispatch (Fed-LTD), a framework that allows effective order dispatching by sharing both dispatching models and decisions while providing privacy protection of raw data and high efficiency. 解决跨平台叫车问题，即多平台在不共享数据的情况下协同进行订单分配。 ↩
Felicitas is a distributed cross-device Federated Learning (FL) framework to solve the industrial difficulties of FL in large-scale device deployment scenarios. Felicitas是一个分布式的跨设备联邦学习（FL）框架，以解决FL在大规模设备部署场景中的工业困难。 ↩
InclusiveFL is to assign models of different sizes to clients with different computing capabilities, bigger models for powerful clients and smaller ones for weak clients. InclusiveFL 将不同大小的模型分配给具有不同计算能力的客户，较大的模型用于强大的客户，较小的用于弱小的客户。 ↩
FedAttack a simple yet effective and covert poisoning attack method on federated recommendation, core idea is using globally hardest samples to subvert model training. FedAttack是一种对联邦推荐的简单而有效的隐蔽中毒攻击方法，核心**是利用全局最难的样本来颠覆模型训练。 ↩
PipAttack present a systematic approach to backdooring federated recommender systems for targeted item promotion. The core tactic is to take advantage of the inherent popularity bias that commonly exists in data-driven recommenders. PipAttack 提出了一种系统化的方法，为联邦推荐系统提供后门，以实现目标项目的推广。其核心策略是利用数据驱动的推荐器中普遍存在的固有的流行偏见。 ↩
Fed2, a feature-aligned federated learning framework to resolve this issue by establishing a firm structure-feature alignment across the collaborative models. Fed2是一个特征对齐的联邦学习框架，通过在协作模型之间建立牢固的结构-特征对齐来解决这个问题。 ↩
FedRS focus on a special kind of non-iid scene, i.e., label distribution skew, where each client can only access a partial set of the whole class set. Considering top layers of neural networks are more task-specific, we advocate that the last classification layer is more vulnerable to the shift of label distribution. Hence, we in-depth study the classifier layer and point out that the standard softmax will encounter several problems caused by missing classes. As an alternative, we propose “Restricted Softmax" to limit the update of missing classes’ weights during the local procedure. FedRS专注于一种特殊的非iid场景，即标签分布倾斜，每个客户端只能访问整个类集的部分集合。考虑到神经网络的顶层更具有任务针对性，我们主张最后一个分类层更容易受到标签分布偏移的影响。因此，我们深入研究了分类器层，并指出标准的softmax会遇到由缺失类引起的一些问题。作为一个替代方案，提出了 "限制性Softmax"，以限制在本地程序中对缺失类的权重进行更新。 ↩
While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework. In this work, we study these barriers and address them by proposing a novel approach Federated Adversarial DEbiasing (FADE). FADE does not require users' sensitive group information for debiasing and offers users the freedom to opt-out from the adversarial component when privacy or computational costs become a concern. 虽然对抗性学习通常用于集中式学习以减轻偏见，但当把它扩展到联邦式框架中时，会有很大的障碍。在这项工作中，我们研究了这些障碍，并通过提出一种新的方法 Federated Adversarial DEbiasing（FADE）来解决它们。FADE不需要用户的敏感群体信息来进行去偏，并且当隐私或计算成本成为一个问题时，用户可以自由地选择退出对抗性部分。 ↩
To address the challenges of communication and computation resource utilization, we propose an asynchronous stochastic quasi-Newton (AsySQN) framework for Vertical federated learning(VFL), under which three algorithms, i.e. AsySQN-SGD, -SVRG and -SAGA, are proposed. The proposed AsySQN-type algorithms making descent steps scaled by approximate (without calculating the inverse Hessian matrix explicitly) Hessian information convergence much faster than SGD-based methods in practice and thus can dramatically reduce the number of communication rounds. Moreover, the adopted asynchronous computation can make better use of the computation resource. We theoretically prove the convergence rates of our proposed algorithms for strongly convex problems. 为了解决通信和计算资源利用的挑战，我们提出了一个异步随机准牛顿（AsySQN）的纵和联邦学习VFL框架，在这个框架下，我们提出了三种算法，即AsySQN-SGD、-SVRG和-SAGA。所提出的AsySQN型算法使下降步骤按近似（不明确计算逆Hessian矩阵）Hessian信息收敛的速度比基于SGD的方法在实践中快得多，因此可以极大地减少通信轮数。此外，采用异步计算可以更好地利用计算资源。我们从理论上证明了我们提出的算法在强凸问题上的收敛率。 ↩
A simple yet effective algorithm, named Federated Learning on Medical Datasets using Partial Networks (FLOP), that shares only a partial model between the server and clients. 一种简单而有效的算法，被命名为使用部分网络的医学数据集的联邦学习（FLOP），该算法在服务器和客户之间只共享部分模型。 ↩
This paper have built a framework that enables Federated Learning (FL) for a small number of stakeholders. and described the framework architecture, communication protocol, and algorithms. 本文建立了一个框架，为少数利益相关者实现联邦学习（FL），并描述了框架架构、通信协议和算法。 ↩
A novel Federated Deep Knowledge Tracing (FDKT) framework to collectively train high-quality Deep Knowledge Tracing (DKT) models for multiple silos. 一个新颖的联邦深度知识追踪（FDKT）框架，为多个筒仓集体训练高质量的深度知识追踪（DKT）模型。 ↩
FedFast accelerates distributed learning which achieves good accuracy for all users very early in the training process. We achieve this by sampling from a diverse set of participating clients in each training round and applying an active aggregation method that propagates the updated model to the other clients. Consequently, with FedFast the users benefit from far lower communication costs and more accurate models that can be consumed anytime during the training process even at the very early stages. FedFast加速了分布式学习，在训练过程的早期为所有用户实现了良好的准确性。我们通过在每轮训练中从不同的参与客户中取样，并应用主动聚合方法，将更新的模型传播给其他客户来实现这一目标。因此，有了FedFast，用户可以从更低的通信成本和更准确的模型中受益，这些模型可以在训练过程中随时使用，即使是在最早期阶段。 ↩
FDSKL, a federated doubly stochastic kernel learning algorithm for vertically partitioned data. Specifically, we use random features to approximate the kernel mapping function and use doubly stochastic gradients to update the solutions, which are all computed federatedly without the disclosure of data. FDSKL，一个针对纵向分割数据的联邦双随机核学习算法。具体来说，我们使用随机特征来近似核映射函数，并使用双重随机梯度来更新解决方案，这些都是在不透露数据的情况下联邦计算的。 ↩
Federated Online Learning to Rank setup (FOLtR) where on-mobile ranking models are trained in a way that respects the users' privacy. FOLtR-ES that satisfies these requirement: (a) preserving the user privacy, (b) low communication and computation costs, (c) learning from noisy bandit feedback, and (d) learning with non-continuous ranking quality measures. A part of FOLtR-ES is a privatization procedure that allows it to provide ε-local differential privacy guarantees, i.e. protecting the clients from an adversary who has access to the communicated messages. This procedure can be applied to any absolute online metric that takes finitely many values or can be discretized to a finite domain. 联邦在线学习排名设置（FOLtR）中，移动端排名模型是以尊重用户隐私的方式来训练的。FOLtR-ES满足这些要求：(a)保护用户隐私，(b)低通信和计算成本，(c)从嘈杂的强盗反馈中学习，以及(d)用非连续的排名质量指标学习。FOLtR-ES的一部分是一个私有化程序，使其能够提供ε-local差异化的隐私保证，即保护客户不受能够接触到通信信息的对手的伤害。这个程序可以应用于任何绝对在线度量，其取值有限，或者可以离散到一个有限域。 ↩
Federated learning is vulnerable to poisoning attacks in which malicious clients poison the global model via sending malicious model updates to the server. Existing defenses focus on preventing a small number of malicious clients from poisoning the global model via robust federated learning methods and detecting malicious clients when there are a large number of them. However, it is still an open challenge how to recover the global model from poisoning attacks after the malicious clients are detected. A naive solution is to remove the detected malicious clients and train a new global model from scratch using the remaining clients. However, such train-from-scratch recovery method incurs a large computation and communication cost, which may be intolerable for resource-constrained clients such as smartphones and IoT devices. In this work, we propose FedRecover, a method that can recover an accurate global model from poisoning attacks with a small computation and communication cost for the clients. Our key idea is that the server estimates the clients’ model updates instead of asking the clients to compute and communicate them during the recovery process. In particular, the server stores the historical information, including the global models and clients’ model updates in each round, when training the poisoned global model before the malicious clients are detected. During the recovery process, the server estimates a client’s model update in each round using its stored historical information. Moreover, we further optimize FedRecover to recover a more accurate global model using warm-up, periodic correction, abnormality fixing, and final tuning strategies, in which the server asks the clients to compute and communicate their exact model updates. Theoretically, we show that the global model recovered by FedRecover is close to or the same as that recovered by train-from-scratch under some assumptions. 联合学习很容易受到中毒攻击，即恶意客户通过向服务器发送恶意的模型更新来毒害全局模型。现有的防御措施主要是通过强大的联合学习方法来防止少量的恶意客户毒害全局模型，并在有大量的恶意客户时检测他们。然而，在检测到恶意客户后，如何从中毒攻击中恢复全局模型仍然是一个公开的挑战。一个天真的解决方案是删除检测到的恶意客户，然后用剩下的客户从头开始训练一个新的全局模型。然而，这种从头开始训练的恢复方法会产生大量的计算和通信成本，这对于资源受限的客户（如智能手机和物联网设备）来说可能是不可容忍的。在这项工作中，我们提出了FedRecover，一种可以从中毒攻击中恢复准确的全局模型的方法，而客户的计算和通信成本却很小。我们的关键想法是，服务器估计客户的模型更新，而不是要求客户在恢复过程中进行计算和通信。特别是，在恶意客户被发现之前，服务器在训练中毒的全局模型时，储存了历史信息，包括全局模型和客户在每一轮的模型更新。在恢复过程中，服务器利用其存储的历史信息估计客户在每一轮的模型更新。此外，我们进一步优化FedRecover，使用预热、定期修正、异常修复和最终调整策略来恢复更准确的全局模型，其中服务器要求客户计算并传达他们的准确模型更新。理论上，我们表明FedRecover恢复的全局模型在某些假设条件下接近或与从头开始训练恢复的模型相同。 ↩
We are motivated to resolve the above issue by proposing a solution, referred to as PEA (Private, Efficient, Accurate), which consists of a secure differentially private stochastic gradient descent (DPSGD for short) protocol and two optimization methods. First, we propose a secure DPSGD protocol to enforce DPSGD, which is a popular differentially private machine learning algorithm, in secret sharing-based MPL frameworks. Second, to reduce the accuracy loss led by differential privacy noise and the huge communication overhead of MPL, we propose two optimization methods for the training process of MPL. 提出一个安全差分隐私随机梯度下降协议以在基于秘密共享的安全多方学习框架中实现差分隐私随机梯度下降算法。为了降低差分隐私带来的精度损失并提升安全多方学习的效率，从安全多方学习训练过程的角度提出了两项优化方法，多方可以在MPL模型训练过程中平衡。做到隐私、效率和准确性三者之间的权衡。 ↩
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This paper studies a new challenging problem, namely few-shot model agnostic federated learning, where the local participants design their independent models from their limited private datasets. Considering the scarcity of the private data, we propose to utilize the abundant public available datasets for bridging the gap between local private participants. However, its usage also brings in two problems: inconsistent labels and large domain gap between the public and private datasets. To address these issues, this paper presents a novel framework with two main parts: 1) model agnostic federated learning, it performs public-private communication by unifying the model prediction outputs on the shared public datasets; 2) latent embedding adaptation, it addresses the domain gap with an adversarial learning scheme to discriminate the public and private domains. 本文研究了一个新的具有挑战性的问题，即少量模型不可知的联合学习，其中本地参与者从他们有限的私人数据集中设计他们的独立模型。考虑到私有数据的稀缺性，我们建议利用丰富的公共数据集来弥合本地私有参与者之间的差距。然而，它的使用也带来了两个问题：不一致的标签和公共和私人数据集之间的巨大领域差距。为了解决这些问题，本文提出了一个新颖的框架，包括两个主要部分：1）模型不可知的联合学习，它通过统一共享的公共数据集上的模型预测输出来进行公私交流；2）潜在嵌入适应，它通过对抗性学习方案来解决领域差距问题，以区分公共和私人领域。 ↩
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Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. FedSAM investigate such behavior through the lens of geometry of the loss and Hessian eigenspectrum, linking the model's lack of generalization capacity to the sharpness of the solution. 联邦学习环境下训练的模型经常会出现性能下降和泛化失败的情况，特别是在面对异质场景时。FedSAM 通过损失和Hessian特征谱的几何角度来研究这种行为，将模型缺乏泛化能力与解决方案的锐度联系起来 ↩
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LC-Fed propose a personalized federated framework with Local Calibration, to leverage the inter-site in-consistencies in both feature- and prediction- levels to boost the segmentation. LC-Fed提出了一个带有本地校准的个性化联邦学习框架，以利用特征和预测层面的站点间不一致来提高分割效果。 ↩
ATPFL helps users federate multi-source trajectory datasets to automatically design and train a powerful TP model. ATPFL帮助用户联邦多源轨迹数据集，自动设计和训练强大的TP轨迹预测模型。 ↩
ViT-FL demonstrate that self-attention-based architectures (e.g., Transformers) are more robust to distribution shifts and hence improve federated learning over heterogeneous data. ViT-FL证明了基于自注意力机制架构（如 Transformers）对分布的转变更加稳健，从而改善了异构数据的联邦学习。 ↩
FedCorr, a general multi-stage framework to tackle heterogeneous label noise in FL, without making any assumptions on the noise models of local clients, while still maintaining client data privacy. FedCorr 一个通用的多阶段框架来处理FL中的异质标签噪声，不对本地客户的噪声模型做任何假设，同时仍然保持客户数据的隐私。 ↩
FedCor, an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. FedCor 一个建立在基于相关性的客户选择策略上的FL框架，以提高FL的收敛率。 ↩
A novel pFL training framework dubbed Layer-wised Personalized Federated learning (pFedLA) that can discern the importance of each layer from different clients, and thus is able to optimize the personalized model aggregation for clients with heterogeneous data. "层级个性化联邦学习"（pFedLA），它可以从不同的客户那里分辨出每一层的重要性，从而能够为拥有异质数据的客户优化个性化的模型聚合。 ↩
FedAlign rethinks solutions to data heterogeneity in FL with a focus on local learning generality rather than proximal restriction. 我们重新思考FL中数据异质性的解决方案，重点是本地学习的通用性(generality)而不是近似限制。 ↩
Position-Aware Neurons (PANs) , fusing position-related values (i.e., position encodings) into neuron outputs, making parameters across clients pre-aligned and facilitating coordinate-based parameter averaging. 位置感知神经元（PANs）将位置相关的值（即位置编码）融合到神经元输出中，使各客户的参数预先对齐，并促进基于坐标的参数平均化。 ↩
Federated semi-supervised learning (FSSL) aims to derive a global model by training fully-labeled and fully-unlabeled clients or training partially labeled clients. RSCFed presents a Random Sampling Consensus Federated learning, by considering the uneven reliability among models from fully-labeled clients, fully-unlabeled clients or partially labeled clients. 联邦半监督学习（FSSL）旨在通过训练有监督和无监督的客户或半监督的客户来得出一个全局模型。随机抽样共识联邦学习，即RSCFed，考虑来自有监督的客户、无监督的客户或半监督的客户的模型之间不均匀的可靠性。 ↩
FCCL (Federated Cross-Correlation and Continual Learning) For heterogeneity problem, FCCL leverages unlabeled public data for communication and construct cross-correlation matrix to learn a generalizable representation under domain shift. Meanwhile, for catastrophic forgetting, FCCL utilizes knowledge distillation in local updating, providing inter and intra domain information without leaking privacy. FCCL（联邦交叉相关和持续学习）对于异质性问题，FCCL利用未标记的公共数据进行交流，并构建交叉相关矩阵来学习领域转移下的可泛化表示。同时，对于灾难性遗忘，FCCL利用局部更新中的知识提炼，在不泄露隐私的情况下提供域间和域内信息。 ↩
RHFL (Robust Heterogeneous Federated Learning) simultaneously handles the label noise and performs federated learning in a single framework. RHFL（稳健模型异构联邦学习），它同时处理标签噪声并在一个框架内执行联邦学习。 ↩
ResSFL, a Split Federated Learning Framework that is designed to be MI-resistant during training. ResSFL一个分割学习的联邦学习框架，它被设计成在训练期间可以抵抗MI模型逆向攻击。 Model Inversion (MI) attack 模型逆向攻击。 ↩
FedDC propose a novel federated learning algorithm with local drift decoupling and correction. FedDC 一种带有本地漂移解耦和校正的新型联邦学习算法。 ↩
Global-Local Forgetting Compensation (GLFC) model, to learn a global class incremental model for alleviating the catastrophic forgetting from both local and global perspectives. 全局-局部遗忘补偿（GLFC）模型，从局部和全局的角度学习一个全局类增量模型来缓解灾难性的遗忘问题。 ↩
FedFTG, a data-free knowledge distillation method to fine-tune the global model in the server, which relieves the issue of direct model aggregation. FedFTG, 一种无数据的知识蒸馏方法来微调服务器中的全局模型，它缓解了直接模型聚合的问题。 ↩
DP-FedAvg+BLUR+LUS study the cause of model performance degradation in federated learning under user-level DP guarantee and propose two techniques, Bounded Local Update Regularization and Local Update Sparsification, to increase model quality without sacrificing privacy. DP-FedAvg+BLUR+LUS 研究了在用户级DP保证下联邦学习中模型性能下降的原因,提出了两种技术，即有界局部更新正则化和局部更新稀疏化，以提高模型质量而不牺牲隐私。 ↩
Generative Gradient Leakage (GGL) validate that the private training data can still be leaked under certain defense settings with a new type of leakage. 生成梯度泄漏（GGL）验证了在某些防御设置下，私人训练数据仍可被泄漏。 ↩
CD2-pFed, a novel Cyclic Distillation-guided Channel Decoupling framework, to personalize the global model in FL, under various settings of data heterogeneity. CD2-pFed，一个新的循环蒸馏引导的通道解耦框架，在各种数据异质性的设置下，在FL中实现全局模型的个性化。 ↩
FedSM propose a novel training framework to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time. 新的训练框架FedSM，以避免客户端漂移问题，并首次成功地缩小了与集中式训练相比在医学图像分割任务中的泛化差距。 ↩
FL-MRCM propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy. FL-MRCM 一个基于联邦学习（FL）的解决方案，其中我们利用了不同机构的MR数据，同时保护了病人的隐私。 ↩
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FedDG-ELCFS A novel problem setting of federated domain generalization (FedDG), which aims to learn a federated model from multiple distributed source domains such that it can directly generalize to unseen target domains. Episodic Learning in Continuous Frequency Space (ELCFS), for this problem by enabling each client to exploit multi-source data distributions under the challenging constraint of data decentralization. FedDG-ELCFS 联邦域泛化（FedDG）旨在从多个分布式源域中学习一个联邦模型，使其能够直接泛化到未见过的目标域中。连续频率空间中的偶发学习（ELCFS），使每个客户能够在数据分散的挑战约束下利用多源数据分布。 ↩
Soteria propose a defense against model inversion attack in FL, learning to perturb data representation such that the quality of the reconstructed data is severely degraded, while FL performance is maintained. Soteria 一种防御FL中模型反转攻击的方法,关键**是学习扰乱数据表示，使重建数据的质量严重下降，而FL性能保持不变。 ↩
FedUFO a Unified Feature learning and Optimization objectives alignment method for non-IID FL. FedUFO 一种针对non IID FL的统一特征学习和优化目标对齐算法。 ↩
FedAD propose a new distillation-based FL frame-work that can preserve privacy by design, while also consuming substantially less network communication resources when compared to the current methods. FedAD 一个新的基于蒸馏的FL框架，它可以通过设计来保护隐私，同时与目前的方法相比，消耗的网络通信资源也大大减少 ↩
FedU a novel federated unsupervised learning framework. FedU 一个新颖的无监督联邦学习框架. ↩
FedUReID, a federated unsupervised person ReID system to learn person ReID models without any labels while preserving privacy. FedUReID，一个联邦的无监督人物识别系统，在没有任何标签的情况下学习人物识别模型，同时保护隐私。 ↩
Introduce two new large-scale datasets for species and landmark classification, with realistic per-user data splits that simulate real-world edge learning scenarios. We also develop two new algorithms (FedVC, FedIR) that intelligently resample and reweight over the client pool, bringing large improvements in accuracy and stability in training. 为物种和地标分类引入了两个新的大规模数据集，每个用户的现实数据分割模拟了真实世界的边缘学习场景。我们还开发了两种新的算法（FedVC、FedIR），在客户池上智能地重新取样和重新加权，在训练中带来了准确性和稳定性的巨大改进 ↩
InvisibleFL propose a privacy-preserving solution that avoids multimedia privacy leakages in federated learning. InvisibleFL 提出了一个保护隐私的解决方案，以避免联邦学习中的多媒体隐私泄漏。 ↩
FedReID implement federated learning to person re-identification and optimize its performance affected by statistical heterogeneity in the real-world scenario. FedReID 实现了对行人重识别任务的联邦学习，并优化了其在真实世界场景中受统计异质性影响的性能。 ↩
Due to the server-client communication and on-device computation bottlenecks, this paper explores whether the big language model can be achieved using cross-device federated learning. First, they investigate quantization and partial model training to address the per round communication and computation cost. Then, they study fast convergence techniques by reducing the number of communication rounds, using transfer learning and centralized pretraining methods. They demonstrated that these techniques, individually or in combination, can scale to larger models in cross-device federated learning. 由于通讯和计算资源受限，他们研究是否能在跨设备联邦学习中训练参数较多的模型，如21M的Transformer, 20.2M的Conformer。首先，他们调查了量化、部分训练技术来减少通讯和计算成本；其次，他们研究快速收敛技术通过减少通讯轮次，运用迁移学习和Centralized pretraining技术。他们的研究表明，运用上述技术，或这些技术的组合，可以在跨设备联邦学习中扩展到更大的模型。 ↩
Communication cost is the largest barrier to the wider adoption of federated learning. This paper addresses this issue by investigating a family of new gradient compression strategies, including static compression, time-varying compression and K-subspace compression. They call it intrinsic gradient compression algorithms. These three gradient compression algorithms can be applied to different levels of bandwidth scenarios and can be used in combination in special scenarios.Moreover, they provide theoretical guarantees on the performance. They train big models with 100M parameters compared to current state-of-the-art gradient compression methods (e.g. FetchSGD). 通讯成本是联邦学习大规模部署面临的最大阻碍。这篇文章研究一系列新的梯度压缩策略来减轻这一挑战，包括static compression, time-varying compression and K-subspace compression，他们称之为intrinstic gradient compression algorighms. 这三种梯度压缩算法可应用于不同级别带宽的场景，在特殊的场景也可以组合使用。而且，他们提供了理论分析保证。他们训练了100M参数的大模型，与其他梯度压缩方法（如FetchSGD）相比，达到SOTA. ↩
Inspired by Bayesian hierarchical models, this paper investigates how to achieve better personalized federated learning by balancing local model improvement and global model tuning. They develop Act-PerFL, a self-aware personalized FL method where leveraging local training and global aggregation via inter- and intra-client uncertainty quantification. Specifically, ActPerFL adaptively adjusts local training steps with automated hyper-parameter selection and performs uncertainty-weighted global aggregation (Non-sample size based weighted average) . 受贝叶斯分层模型的启发，本文研究如何通过平衡本地模型和全局模型实现更好的个性化联邦学习。他们提出了ActPerFL，利用客户间和客户内部的不确定性量化来指导本地训练和全局聚合。具体来说，ActPerFL通过自动超参数选择自适应地调整本次训练次数，并执行不确定性加权全局聚合（非基于样本数量的带权平均）。 ↩
This paper present a benchmarking framework for evaluating federated learning methods on four common formulations of NLP tasks: text classification, sequence tagging, question answering, and seq2seq generation. 联邦学习在NLP领域的一个基准框架，提供常见的联邦学习算法实现（FedAvg、FedProx、FedOPT），支持四种常见NLP任务（文本分类、序列标记、问答、seq2seq）的对比。 ↩
In realistic human-computer interaction, there are usually many noisy user feedback signals. This paper investigates whether federated learning can be trained directly based on positive and negative user feedback. They show that, under mild to moderate noise conditions, incorporating feedback improves model performance over self-supervised baselines.They also study different levels of noise hoping to mitigate the impact of user feedback noise on model performance. 在现实的人机交互中，通常有很多带噪声的用户反馈信号。本文研究是否能直接基于积极和消极的用户反馈来进行联邦学习训练。他们表明，在轻度至中度噪声条件下，与自监督基准相比，结合不同反馈可以提高模型性能。他们还对不同程度的噪声展开研究，希望能减轻用户反馈噪声对模型性能的影响。 ↩
Due to the real-world limitations of centralized training, when training mixed-domain translation models with federated learning, this paper finds that the global aggregation strategy of federated learning can effectively aggregate information from different domains, so that NMT (neural machine translation) can benefit from federated learning. At the same time, they propose a novel and practical solution to reduce the communication bandwidth. Specifically, they design Dynamic Pulling, which pulls only one type of high volatility tensor in each round of communication. 由于中心式训练在现实世界存在诸多限制，在用联邦学习训练mixed-domain translation models时候，本文发现联邦学习的全局聚合策略可以有效融合来自不同领域的信息，使得NMT（neural machine translation）可以从联邦学习中受益。同时由于通信瓶颈，他们提出一种新颖且实用的方案来降低通信带宽。具体来说，他们设计了 Dynamic Pulling, 在每轮通信中只拉取一种类型的高波动张量。 ↩
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In this perspective paper we study the effect of non independent and identically distributed (non-IID) data on federated online learning to rank (FOLTR) and chart directions for future work in this new and largely unexplored research area of Information Retrieval. 在这篇前瞻论文中，我们研究了非独立和相同分布（非IID）数据对联邦在线学习排名（FOLTR）的影响，并为这个新的、基本上未被开发的信息检索研究领域的未来工作指明了方向。 ↩
The cross-domain recommendation problem is formalized under a decentralized computing environment with multiple domain servers. And we identify two key challenges for this setting: the unavailability of direct transfer and the heterogeneity of the domain-specific user representations. We then propose to learn and maintain a decentralized user encoding on each user's personal space. The optimization follows a variational inference framework that maximizes the mutual information between the user's encoding and the domain-specific user information from all her interacted domains. 跨域推荐问题在具有多个域服务器的去中心化计算环境下被形式化。我们确定了这种情况下的两个关键挑战：直接传输的不可用性和特定领域用户表征的异质性。然后，我们建议在每个用户的个人空间上学习和维护一个分散的用户编码。优化遵循一个变分推理框架，使用户的编码和来自她所有互动领域的特定用户信息之间的互信息最大化。 ↩
Under some circumstances, the private data can be reconstructed from the model parameters, which implies that data leakage can occur in FL.In this paper, we draw attention to another risk associated with FL: Even if federated algorithms are individually privacy-preserving, combining them into pipelines is not necessari ↩