Federated Client-tailored Adapter for Medical Image Segmentation

2025-04-25

AI Summary

To address training instability in federated medical image segmentation caused by data silos and domain heterogeneity—specifically distribution shift and class imbalance—this paper proposes a client-customized adapter framework built upon medical foundation models. The method decouples shared generic parameters from client-specific adaptation parameters and introduces a dual-path update strategy, enabling personalized model convergence without sharing raw data. Innovatively integrating lightweight federated adaptation with domain-aware parameter grouping optimization, the approach significantly enhances model robustness and generalization. Experiments on three large-scale X-ray medical datasets demonstrate substantial improvements over existing federated segmentation methods: average Dice score increases by 2.3–4.7 percentage points, and training variance decreases by 38%. The framework establishes a scalable, customizable paradigm for privacy-sensitive, distributed medical image analysis.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Segmentation
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Addressing domain heterogeneity in federated medical image segmentation
• Enabling stable training without sharing sensitive local data
• Achieving client-tailored segmentation models in distributed data scenarios

Innovation

• Federated Client-tailored Adapter for segmentation
• Client-specific federated updating strategies
• Utilizes medical foundation models knowledge