Easy, Basic, and Clean Federated Learning Codebase

How to run the example code

bash run_scripts/example.sh

To run other experiments

• Go to config.py to check the arguments
• In run_scripts/example.sh, there are several common arguments in FL
• Currently supported algorithms: FedAvg / FedProx / SCAFFOLD.

Others

If you find this repo useful, please feel free to cite us.

See other projects and papers at Rui Ye's Homepage.

Recommend the following papers of ours: (codes are not as clean as this repo)

• FedFM (TSP 2023) [Paper] / [Code]

@article{ye2023fedfm,
  title={Fedfm: Anchor-based feature matching for data heterogeneity in federated learning},
  author={Ye, Rui and Ni, Zhenyang and Xu, Chenxin and Wang, Jianyu and Chen, Siheng and Eldar, Yonina C},
  journal={IEEE Transactions on Signal Processing},
  year={2023},
  publisher={IEEE}
}

• FedDisco (ICML 2023) [Paper] / [Code]

@article{ye2023feddisco,
  title={FedDisco: Federated Learning with Discrepancy-Aware Collaboration},
  author={Ye, Rui and Xu, Mingkai and Wang, Jianyu and Xu, Chenxin and Chen, Siheng and Wang, Yanfeng},
  journal={arXiv preprint arXiv:2305.19229},
  year={2023}
}

• pFedGraph (ICML 2023) [Paper] / [Code]

@article{ye2023personalized,
  title={Personalized Federated Learning with Inferred Collaboration Graphs},
  author={Ye, Rui and Ni, Zhenyang and Wu, Fangzhao and Chen, Siheng and Wang, Yanfeng},
  year={2023}
}