This awesome list provides links to relevant papers reviewed in privacy preserving federated learning in medical imaging with uncertainty estimation.
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Federated Optimization in Heterogeneous Networks
- FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
- TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
- NVIDIA FLARE: Federated Learning from Simulation to Real-World
- OpenFL: the open federated learning library
- Swarm Learning for decentralized and confidential clinical machine learning
- Decentralized federated learning through proxy model sharing
- FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis
- A Bayesian Federated Learning Framework with Online Laplace Approximation
- Data-Free Knowledge Distillation for Heterogeneous Federated Learning
- A Fog-Based Privacy-Preserving Federated Learning System for Smart Healthcare Applications
- Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
- Learn from Others and Be Yourself in Heterogeneous Federated Learning
- Personalized Federated Learning with Adaptive Batchnorm for Healthcare
- Privacy-preserving Federated Brain Tumour Segmentation
- Federated Learning with Differential Privacy: Algorithms and Performance Analysis
- Federated learning and differential privacy for medical image analysis
- APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning
- End-to-end privacy-preserving deep learning on multi-institutional medical imaging
- Secure, privacy-preserving and federated machine learning in medical imaging
- DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting
- Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
- Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
- Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification
- Differentially private knowledge transfer for federated learning
- Differential Privacy-enabled Federated Learning for Sensitive Health Data
- Encrypted federated learning for secure decentralized collaboration in cancer image analysis
- FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
- LDS-FL: Loss Differential Strategy Based Federated Learning for Privacy Preserving
- A Hybrid Approach to Privacy-Preserving Federated Learning
- Approaches to Uncertainty Quantification in Federated Deep Learning
- Uncertainty Quantification in Federated Learning for Heterogeneous Health Data
- Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
- Federated Conformal Predictors for Distributed Uncertainty Quantification
- Federated Learning with Uncertainty via Distilled Predictive Distributions
- Distribution-Free Federated Learning with Conformal Predictions
- Fed-ensemble: Ensemble Models in Federated Learning for Improved Generalization and Uncertainty Quantification
- FedUA: An Uncertainty-Aware Distillation-Based Federated Learning Scheme for Image Classification
- Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings
- Self-Aware Personalized Federated Learning
- No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
- Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data
- FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data
- Personalized Federated Learning via Variational Bayesian Inference
- FedPop: A Bayesian Approach for Personalised Federated Learning
- Probabilistic Predictions with Federated Learning