An implementation for "Personalized Federated Learning with Parameter Propagation" (KDD'23).
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
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}
}
Some codes of FEDORA are adapted from the following baselines.
LG-FedAvg: https://github.com/pliang279/LG-FedAvg