Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation

Xingyue Zhao; Wenke Huang; Linghao Zhuang; Yanzhou Su; Zhifeng Wang; Haoyu Zhao; Mengfan Li; Junjun He; Tao Tan; Dakai Jin; Le Lu; Mang Ye; Qiang Yang; Ming Feng

arXiv · 2610.04700

Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation

Xingyue Zhao·Wenke Huang·Linghao Zhuang·Yanzhou Su·Zhifeng Wang·Haoyu Zhao·Mengfan Li·Junjun He·Tao Tan·Dakai Jin·Le Lu·Mang Ye·Qiang Yang·Ming Feng

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Abstract

Federated learning enables medical institutions to train a global model without sharing data, yet feature heterogeneity from diverse scanners or protocols remains challenging. Existing representation-based methods face two limitations: 1) Incomplete Contextual Representation Learning: single-layer or coupled representations overlook multi-level structural cues and entangle regional semantics with boundary details. 2) Layerwise Style and Aggregation Biases: domain-specific style discrepancies across intermediate layers degrade prototypes, while aggregation that overlooks client distribution shifts can further amplify bias. We propose FedBCS+, federated decoupled contextual alignment with style-purified aggregation. We employ Frequency-domain Style Recalibration (FSR) in prototype construction to decouple content-style representations and extract style-purified prototypes. Built upon these purified features, Decoupled Contextual Prototype Alignment (DCPA) explicitly decouples multi-level features into semantic and structural prototypes and aligns regional semantics and fine-grained anatomical structures separately. Style-purified Semantic Prototype Aggregation (S2PA) measures each client's purified prototype divergence from the global consensus and adaptively reweights aggregation toward under-represented clients to reduce consensus bias. On five heterogeneous medical segmentation benchmarks spanning histopathology, MRI, ultrasound, and colonoscopy, FedBCS+ achieves the highest mean Dice among the compared methods. A convergence analysis further characterizes how aggregation and alignment affect the optimization bound.

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Xingyue Zhao, Wenke Huang, Linghao Zhuang, Yanzhou Su, Zhifeng Wang, Haoyu Zhao, Mengfan Li, Junjun He, Tao Tan, Dakai Jin, Le Lu, Mang Ye, Qiang Yang, Ming Feng. 2026-10-03. Decouple, Purify and Unite: Semantic-Structural Prototype Learning for Federated Medical Segmentation. https://arxiv.org/abs/2610.04700

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