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.

医学图像分割中，个性化跨领域联邦学习 (FL) 正变得受欢迎，以利用医疗保健环境中的多样数据来解决数据稀缺和隐私问题。本文提出了一种新的框架，通过特征增强实现个性化联邦学习 (pFLFE)，以减轻这些挑战。通过对三个医学分割任务的实验证明，pFLFE 的性能优于现有的方法。