Introduction

This is the implementation of our paper FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy (accepted by KDD 2023).

• Oral PPT

Citation

@inproceedings{Zhang2023fedcp,
  author = {Zhang, Jianqing and Hua, Yang and Wang, Hao and Song, Tao and Xue, Zhengui and Ma, Ruhui and Guan, Haibing},
  title = {FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy},
  year = {2023},
  booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}
}

TL;DR: Existing personalized federated learning (pFL) mehtods focus on exploiting global and personalized information in model parameters rather than the source of information: data, so we propose FedCP to automatically separate global and personalized information from data (i.e., feature representations) in iterative federated learning procedure, as shown in the following figures.

Dataset

Due to the file size limitation of GitHub repository, we only upload the mnist dataset with the default practical setting ($\beta=0.1$). You can generate other datasets based on my other repository PFLlib.

System

• main.py: configurations of FedCP.
• run_me.sh: start FedCP.
• env_linux.yaml: python environment to run FedCP on Linux.
• ./flcore:
  • ./clients/clientcp.py: the code on the client.
  • ./servers/servercp.py: the code on the server.
  • ./trainmodel/models.py: the code for backbones.
• ./utils:
  • data_utils.py: the code to read the dataset.

Federated Conditional Policy (FedCP)

Simulation

Environments

With the installed conda, we can run this platform in a conda virtual environment called fl_torch.

conda env create -f env_linux.yaml # for Linux

Training and Evaluation

All codes corresponding to FedCP are stored in ./system. Just run the following commands.

cd ./system
sh run_me.sh # for Linux