FedSoup

The official Pytorch implementation of paper "FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation" accepted by MICCAI 2023

Authors: Minghui Chen, Meirui Jiang, Qi Dou, Zehua Wang, Xiaoxiao Li.

Abstract

Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratories. However, recent research has found that current FL algorithms face a trade-off between local and global performance when confronted with distribution shifts. Specifically, personalized FL methods have a tendency to overfit to local data, leading to a sharp valley in the local model and inhibiting its ability to generalize to out-of-distribution data. In this paper, we propose a novel federated model soup method (i.e., selective interpolation of model parameters) to optimize the trade-off between local and global performance. Specifically, during the federated training phase, each client maintains its own global model pool by monitoring the performance of the interpolated model between the local and global models. This allows us to alleviate overfitting and seek flat minima, which can significantly improve the model's generalization performance.

Algorithm Overview

Dataset and Environment Preparation

Tiny Camelyon17 preprocessed dataset download link. Retina preprocessed dataset download link.

pip install -r requirements.txt

Training and Local, Global and Out-of-Domain Performance Evaluation

Prepare logs and saved model directories

mkdir results
cd system
mkdir models

Specify the hold-out client index (e.g., "-hoid 0")

sh scripts/run_tiny_camelyon17_hoid0.sh

Training and Local and Global Performance Evaluation (no hold-out client)

sh scripts/run_tiny_camelyon17.sh

Acknowledgement

We develop FedSoup on top of this personalized federated learning platform.

Citation

If you find this work helpful, feel free to cite our paper as follows:

@misc{chen2023fedsoup,
    title={FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation},
    author={Minghui Chen and Meirui Jiang and Qi Dou and Zehua Wang and Xiaoxiao Li},
    year={2023},
    eprint={2307.10507},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}