bibtext: @InProceedings{ Xie_pFLFE_MICCAI2024,
                   author = { Xie, Luyuan and Lin, Manqing and Liu, Siyuan and Xu, ChenMing and Luan, Tianyu and Li, Cong and Fang, Yuejian and Shen, Qingni and Wu, Zhonghai },
                   title = { { pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15010 },
                   month = {October},
                   pages = { pending },
                }
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.
title: pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
authors: Xie, Luyuan Lin, Manqing Liu, Siyuan Xu, ChenMing Luan, Tianyu Li, Cong Fang, Yuejian Shen, Qingni Wu, Zhonghai