  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.
