jwu4sml/fedora

Personalized Federated Learning with Parameter Propagation

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