pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
This addresses data scarcity and privacy issues in healthcare settings by improving segmentation performance, though it appears incremental as it builds on existing federated learning approaches.
The paper tackles client drift and inconsistent performance in personalized cross-silo federated learning for medical image segmentation by proposing pFLFE, which uses feature enhancement and supervised learning stages, and shows it outperforms state-of-the-art methods in experiments on three tasks.
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.