This repo is a collection of AWESOME things about federated learning, including papers, code, etc. Feel free to star and fork.
- Federated Learning on Non-IID Data Silos: An Experimental Study [Arxiv2021]
- Federated Learning: Challenges, Methods, and Future Directions [SPM2020]
- Prototype Guided Federated Learning of Visual Feature Representations [Arxiv2021]
- Federated Learning with Fair Averaging [IJCAI2021]
- Federated Learning with Matched Averaging [ICLR2020][Pytorch]
- Fair Resource Allocation in Federated Learning [ICLR2020]
- Model Fusion via Optimal Transport [NIPS2020][Pytorch]
- Ensemble Distillation for Robust Model Fusion in Federated Learning [NIPS2020]
- FedAvg: Communication-Efficient Learning of Deep Networks from Decentralized Data [AISTATS2017][Pytorch]
- Model-Contrastive Federated Learning [CVPR2021][Pytorch]
- FedBN: Federated Learning on Non-IID Features via Local Batch Normalization [ICLR2021][Pytorch]
- FedAMP: Personalized Cross-Silo Federated Learning on Non-IID Data [AAAI2021][API]
- FedNova: Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization [NIPS2020][Pytorch]
- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning [ICML2020]
- FedProx: Federated Optimization in Heterogeneous Networks [MLSys2020][Pytorch]
- Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach [NIPS2020]
- Personalized Federated Learning with Moreau Envelopes [NIPS2020]
- Federated Visual Classification with Real-World Data Distribution [ECCV2020]
- Federated Mutual Learning [Arxiv2020]
- On the Convergence of FedAvg on Non-IID Data [ICLR2020]
- See through Gradients: Image Batch Recovery via GradInversion [CVPR 2021]
- Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective [CVPR2021]
- Inverting Gradients - How easy is it to break privacy in federated learning? [NIPS2020]
- The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks [CVPR2020]
- Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion [CVPR 2020][Pytorch]
- Deep Leakage from Gradients [NIPS2019][Pytorch]
- iDLG: Improved Deep Leakage from Gradients [Arxiv2020]
- Inverting Deep Generative models, One layer at a time [NIPS2019]
- Addressing Class Imbalance in Federated Learning [AAAI2021][Pytorch]
- Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems [TPDS2021]
- Learn distributed GAN with Temporary Discriminators [ECCV2020]
- Federated Adversarial Domain Adaptation [ICLR2020] Discriminators [ECCV2020][Pytorch]
- FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space [CVPR2021][Pytorch]
- Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning [CVPR2021][Pytorch]
- Privacy-Preserving Constrained Domain Generalization for Medical Image Classification [Arxiv2021]
- The Federated Tumor Segmentation (FeTS) Challenge [Arxiv2021]
- FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation [Arxiv2021]
- Peer Learning for Skin Lesion Classification [Arxiv2021]
- Inverse Distance Aggregation for Federated Learning with Non-IID Data [MICCAI2020 Workshop]
- Siloed Federated Learning for Multi-Centric Histopathology Datasets [MICCAI2020 Workshop]
- Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results [MIA2020][Pytorch]
- Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan [MIA2020]
- Variation-Aware Federated Learning with Multi-Source Decentralized Medical Image Data [JBHI2020]