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초록

Medical image federated learning has emerged as a practical solution for multi-center collaboration without centralizing sensitive data. However, the dominant source of heterogeneity in medical imaging is often not merely at the statistical level but also at the mechanism level, arising from scanner vendors, acquisition protocols, reconstruction pipelines, and annotation styles. Such heterogeneity encourages models to rely on site-specific shortcuts rather than pathology-relevant signals, which leads to poor external-site generalization. To address this problem, we propose CarPe-FL, which is a causal representation-based personalized federated learning framework for medical imaging. CarPe-FL maps images into a latent factor space, estimates client-specific latent causal structures under server-side management, clusters institutions according to structural similarity, and constructs cluster-wise global causal backbones. These backbones are then injected into federated representation learning through structure-aligned masking and edge-wise personalization, while personalized heads capture institution-specific prediction behavior. In this way, CarPe-FL aims to suppress shortcut-dependent pathways while preserving clinically meaningful local adaptation. The proposed framework is expected to provide a principled solution for robust, personalized, and interpretable federated learning in multi-center medical imaging.

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