MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis

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.