首个面向医疗图像分割的个性化联邦SAM框架，解决隐私与异构数据难题。
pFedSAM: Personalized Federated Learning of Segment Anything Model for Medical Image Segmentation
- 只聚合全局参数，保留局部专家模块以适应不同医学领域特征。
- 通过师生知识蒸馏实现全局与本地模型协同优化，提升跨域适应性。
- 在两个公开数据集上显著提升分割效果，通信开销更低，适合多机构协作。
医疗图像分割对辅助诊断至关重要，但隐私限制阻碍了机构间的数据共享。联邦学习虽能缓解此问题，但现有方法多依赖轻量架构，在复杂异构数据上表现不佳。最近的通用图像分割模型SAM展现出卓越性能，但其庞大编码器在联邦设置中面临挑战。本文提出首个针对医疗图像分割异构数据场景的个性化联邦SAM框架。该框架包含两项关键创新：（1）个性化策略仅聚合全局参数以捕捉跨客户端共性，同时保留设计的L-MoE（局部混合专家）组件以保持领域特异性特征；（2）解耦的全局-局部微调机制，采用教师-学生范式结合知识蒸馏，弥合全局共享模型与个性化本地模型之间的差距，从而缓解过拟合。在两个公开数据集上的大量实验表明，该方法显著提升分割性能，实现鲁棒的跨域适应，并降低通信开销。
原文摘要 · Abstract (English)
Medical image segmentation is crucial for computer-aided diagnosis, yet privacy constraints hinder data sharing across institutions. Federated learning addresses this limitation, but existing approaches often rely on lightweight architectures that struggle with complex, heterogeneous data. Recently, the Segment Anything Model (SAM) has shown outstanding segmentation capabilities; however, its massive encoder poses significant challenges in federated settings. In this work, we present the first personalized federated SAM framework tailored for heterogeneous data scenarios in medical image segmentation. Our framework integrates two key innovations: (1) a personalized strategy that aggregates only the global parameters to capture cross-client commonalities while retaining the designed L-MoE (Localized Mixture-of-Experts) component to preserve domain-specific features; and (2) a decoupled global-local fine-tuning mechanism that leverages a teacher-student paradigm via knowledge distillation to bridge the gap between the global shared model and the personalized local models, thereby mitigating overgeneralization. Extensive experiments on two public datasets validate that our approach significantly improves segmentation performance, achieves robust cross-domain adaptation, and reduces communication overhead.
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