pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation

TL;DR

pFLFE introduces a novel federated learning framework for medical image segmentation that enhances feature differentiation and reduces communication rounds, leading to improved performance across multiple tasks.

Contribution

The paper proposes pFLFE, a new personalized federated learning method with feature enhancement and efficient training, addressing client drift and communication limitations.

Findings

pFLFE outperforms existing methods on three medical segmentation tasks.

Enhanced feature differentiation improves segmentation accuracy.

Fewer communication rounds are required without sacrificing quality.

Abstract

In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity and privacy concerns. However, existing methods often suffer from client drift, leading to inconsistent performance and delayed training. We propose a new framework, Personalized Federated Learning via Feature Enhancement (pFLFE), designed to mitigate these challenges. pFLFE consists of two main stages: feature enhancement and supervised learning. The first stage improves differentiation between foreground and background features, and the second uses these enhanced features for learning from segmentation masks. We also design an alternative training approach that requires fewer communication rounds without compromising segmentation quality, even with limited communication resources. Through experiments on three medical segmentation tasks, we demonstrate that pFLFE outperforms the state-of-the-art methods.