Personalized Federated Learning with Adaptive Batchnorm for Healthcare

TL;DR

This paper introduces FedAP, a personalized federated learning method for healthcare that uses adaptive batch normalization to handle non-iid data and improve model accuracy across diverse clients.

Contribution

FedAP is a novel approach that models client similarity via batch normalization statistics, enabling personalized models in healthcare federated learning.

Findings

Achieves 10% accuracy improvement on PAMAP2 benchmark.

Faster convergence compared to existing methods.

Effectively handles non-iid data in healthcare settings.

Abstract

There is a growing interest in applying machine learning techniques to healthcare. Recently, federated learning (FL) is gaining popularity since it allows researchers to train powerful models without compromising data privacy and security. However, the performance of existing FL approaches often deteriorates when encountering non-iid situations where there exist distribution gaps among clients, and few previous efforts focus on personalization in healthcare. In this article, we propose FedAP to tackle domain shifts and then obtain personalized models for local clients. FedAP learns the similarity between clients based on the statistics of the batch normalization layers while preserving the specificity of each client with different local batch normalization. Comprehensive experiments on five healthcare benchmarks demonstrate that FedAP achieves better accuracy compared to state-of-the-art methods (e.g., 10% accuracy improvement for PAMAP2) with faster convergence speed.