bibtext: @InProceedings{ Xie_MHpFLGB_MICCAI2024,
                   author = { Xie, Luyuan and Lin, Manqing and Xu, ChenMing and Luan, Tianyu and Zeng, Zhipeng and Qian, Wenjun and Li, Cong and Fang, Yuejian and Shen, Qingni and Wu, Zhonghai },
                   title = { { MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis } },
                   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 the evolving application of medical artificial intelligence, federated learning stands out for its capacity to protect the privacy of training data, enabling collaborative model development without sharing local data from healthcare entities. However, the heterogeneity of data and systems across institutions presents significant challenges, undermining the efficiency of federated learning and the exchange of information between clients. To address these issues, we introduce a novel approach, MH-pFLGB, which employs a global bypass strategy to mitigate the reliance on public datasets and navigate the complexities of non-IID data distributions. Our method enhances traditional federated learning by integrating a global bypass model, which would share the information among the client, but also serves as part of the network to enhance the performance on each client. Additionally, \model provides a feature fusion module to better combine the local and global features. We validate MH-pFLGB’s effectiveness and adaptability through extensive testing on different medical tasks, demonstrating superior performance compared to existing state-of-the-art methods.
title: MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis
authors: Xie, Luyuan Lin, Manqing Xu, ChenMing Luan, Tianyu Zeng, Zhipeng Qian, Wenjun Li, Cong Fang, Yuejian Shen, Qingni Wu, Zhonghai