Federated Client-tailored Adapter for Medical Image Segmentation

• URL: http://arxiv.org/abs/2504.18020v1
• Date: Fri, 25 Apr 2025 02:20:25 GMT
• Title: Federated Client-tailored Adapter for Medical Image Segmentation
• Authors: Guyue Hu, Siyuan Song, Yukun Kang, Zhu Yin, Gangming Zhao, Chenglong Li, Jin Tang,
• Abstract summary: We propose a novel Federated Client-tailored Adapter (FCA) framework for medical image segmentation.<n>FCA achieves stable and client-tailored adaptive segmentation without sharing sensitive local data.<n>We develop two client-tailored federated updating strategies that adaptively decompose the adapter into common and individual components.
• Score: 21.964553228831427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image segmentation in X-ray images is beneficial for computer-aided diagnosis and lesion localization. Existing methods mainly fall into a centralized learning paradigm, which is inapplicable in the practical medical scenario that only has access to distributed data islands. Federated Learning has the potential to offer a distributed solution but struggles with heavy training instability due to client-wise domain heterogeneity (including distribution diversity and class imbalance). In this paper, we propose a novel Federated Client-tailored Adapter (FCA) framework for medical image segmentation, which achieves stable and client-tailored adaptive segmentation without sharing sensitive local data. Specifically, the federated adapter stirs universal knowledge in off-the-shelf medical foundation models to stabilize the federated training process. In addition, we develop two client-tailored federated updating strategies that adaptively decompose the adapter into common and individual components, then globally and independently update the parameter groups associated with common client-invariant and individual client-specific units, respectively. They further stabilize the heterogeneous federated learning process and realize optimal client-tailored instead of sub-optimal global-compromised segmentation models. Extensive experiments on three large-scale datasets demonstrate the effectiveness and superiority of the proposed FCA framework for federated medical segmentation.

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