FEDORA

An implementation for "Personalized Federated Learning with Parameter Propagation" (KDD'23).

Environment Requirements

The code has been tested under Python 3.7.4. The required packages are as follows:

• numpy==1.21.6
• torch==1.13.1+cu117
• torchvision==0.14.1+cu117
• tqdm==4.66.1

Acknowledgement

This is the latest source code of FEDORA for KDD-2023. If you find that it is helpful for your research, please consider citing our paper:

@inproceedings{wu2023personalized,
  title={Personalized Federated Learning with Parameter Propagation},
  author={Wu, Jun and Bao, Wenxuan and Ainsworth, Elizabeth and He, Jingrui},
  booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={2594--2605},
  year={2023}
}

Reference

Some codes of FEDORA are adapted from the following baselines.

LG-FedAvg: https://github.com/pliang279/LG-FedAvg

pFedHN: https://github.com/AvivSham/pFedHN