跨机构病理切片分析中，用合成特征对齐实现隐私保护的协同训练。
Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration
- 各机构本地用高斯混合模型对齐真实切片特征分布，生成语义丰富的合成特征。
- 通过渐进式融合策略，在性能停滞时引入跨机构合成特征，提升诊断准确率。
- 无需交换原始数据或参数，支持异构架构，适合多中心医疗协作。
联邦学习（FL）为跨机构数字病理协作提供了前景广阔的框架。然而实际部署面临多种实例学习（MIL）架构及异构特征提取器带来的异质性问题。本文提出FedHD，一种针对全切片图像（WSI）分析的新型联邦学习框架，采用本地高斯混合特征对齐机制。各客户端独立蒸馏出与真实WSI分布一致的语义丰富合成特征表示，不直接交换模型参数。为保留诊断多样性，采用一对一蒸馏策略，为每张真实切片生成对应合成副本，避免过度压缩。联邦阶段采用基于课程的学习整合策略，当本地性能趋于稳定时，逐步将跨站点合成特征融入本地训练。此外，可选的解释模块可从合成嵌入重建伪切片，提升模型透明度。FedHD具有架构无关性、隐私保护性，并支持个性化与协作化联合训练。在TCGA-IDH、CAMELYON16和CAMELYON17数据集上的实验表明，其性能持续优于当前最先进的联邦学习与蒸馏基线。
原文摘要 · Abstract (English)
Federated learning (FL) offers a promising framework for collaborative digital pathology by enabling model training across institutions. However, real-world deployments face heterogeneity arising from diverse multiple instance learning (MIL) architectures and heterogeneous feature extractors across institutions. We propose FedHD, a novel FL framework that performs local Gaussian-mixture feature alignment tailored for WSI analysis. Instead of exchanging model parameters, each client independently distills semantically rich synthetic feature representations aligned with the distribution of real WSIs. To preserve diagnostic diversity, FedHD adopts a one-to-one distillation strategy, generating a synthetic counterpart for each real slide to avoid over-compression. During federation, a curriculum-based integration strategy progressively incorporates cross-site synthetic features into local training once performance plateaus. Furthermore, an optional interpretation module reconstructs pseudo-patches from synthetic embeddings, enhancing transparency. FedHD is architecture-agnostic, privacy-preserving, and supports personalized yet collaborative training across diverse institutions. Experiments on TCGA-IDH, CAMELYON16, and CAMELYON17 show that FedHD consistently outperforms state-of-the-art federated and distillation baselines.
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