Awesome Asynchronous Federated Learning

Collect some Asynchronous Federated Learning papers.

Please give me a ⭐star if you find it useful (❁´◡`❁).

If you find some overlooked papers, please open issues or pull requests(recommended), following the Contributing section.

Last Update: Jul 08, 2022 10:43:37

Basic

[FedAvg] Communication-Efficient Learning of Deep Networks from Decentralized Data(AISTAT) [PDF]

Benchmarks

[LEAF] Leaf: A benchmark for federated settings(arXiv) [PDF] [GitHub]

Libraries(Which support Asynchronous Federated Learning)

[FedML] FedML: A Research Library and Benchmark for Federated Machine Learning(arXiv) [Home] [PDF] [GitHub] [Docs]
[FedHF] FedHF: 🔨 A Flexible Federated Learning Simulator. [GitHub]
[FederatedScope] FederatedScope: A Flexible Federated Learning Platform for Heterogeneity [Home] [GitHub] [PDF]
[PySyft] PySyft: A Library for Easy Federated Learning(Studies in Computational Intelligence) [GitHub] [PDF]
[FedLab] FedLab: A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research. [GitHub] [Docs]

Survey

[Open Problem] Advances and Open Problems in Federated Learning(FnTML) [PDF]
Asynchronous Federated Learning on Heterogeneous Devices: A Survey(arXiv) [PDF]

Theory

On the Convergence of FedAvg on Non-IID Data(ICLR 2020) [PDF] [GitHub]

Heterogeneous Network

[FedProx] Federated Optimization in Heterogeneous Networks(MLSys 2020) [PDF] [GitHub]

Tier-based

[TiFL] TiFL: A Tier-based Federated Learning System(HPDC 2020) [PDF]
[FedAT] FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers(arXiv) [PDF]

Asynchronous

2022

[AsyncFedED] AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation(arXiv) [PDF]

2021

[FedSA] FedSA: A staleness-aware asynchronous Federated Learning algorithm with non-IID data(FGCS Elsevier) [PDF]
[SAFA] SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead(IEEE Transactions on Computers) [PDF]
[FedDR] FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization(ResearchGate) [PDF]
[AFSGD-VP] Privacy-Preserving Asynchronous Vertical Federated Learning Algorithms for Multiparty Collaborative Learning(TNNLS) [PDF]
An Asynchronous Federated Learning Approach for a Security Source Code Scanner(ICISSP 2021) [PDF]
[FedConD] Asynchronous Federated Learning for Sensor Data with Concept Drift(arXiv) [PDF]
[FedBuff] Federated Learning with Buffered Asynchronous Aggregation(arXiv) [PDF]

2020

Adaptive Task Allocation for Asynchronous Federated and Parallelized Mobile Edge Learning(arXiv) [PDF]
[ASO-Fed] Asynchronous Online Federated Learning for Edge Devices with Non-IID Data(Big Data 2020) [PDF]
[VAFL] VAFL: a Method of Vertical Asynchronous Federated Learning(ICML 2020) [PDF]

2019

[FedAsync] Asynchronous Federated Optimization(OPT 2020) [PDF]
[DP-AFL] Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics(TII) [PDF]

2018

Asynchronous Federated Learning for Geospatial Applications(ECML PKDD 2018) [PDF]
Federated learning for ultra-reliable low-latency V2V communications(GLOBECOM) [PDF]

Blog

In progress...

Contributing

You can contribute to this project by opening an issue or creating a pull request on GitHub.

Add paper to the papers.yaml file with the following format:

- title: "Communication-Efficient Learning of Deep Networks from Decentralized Data"
  abbr: FedAvg
  year: 2016
  conf: AISTAT
  links:
    PDF: https://arxiv.org/abs/1602.05629.pdf
    GitHub:

Citations

@misc{awesomeafl,
    title = {awesome-asyncrhonous-federated-learning},
    author = {Bingjie Yan},
    year = {2022},
    howpublished = {\\url{https://github.com/beiyuouo/awesome-asynchronous-federated-learning}
}

ZCyueternal / awesome-asynchronous-federated-learning