用可学习原型缓解医疗影像联邦学习中的客户端漂移问题。
FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging
- 用多个可学习原型捕捉跨客户端的共享语义结构。
- 在非独立同分布数据下，准确率分别达81.1%和95.8%，优于基线。
- 适合处理多中心医疗影像数据，提升模型泛化能力。
联邦学习因扫描设备、采集协议和患者群体差异导致的数据非独立同分布（non-IID）问题而受限。此类异质性引发本地优化过程中的客户端漂移，使参数聚合后模型收敛不稳且性能欠佳。本文提出一种基于原型的影响感知联邦学习框架FedProIn，通过多个可学习类别原型捕捉跨异构客户端的共享语义结构。引入特征分歧损失与原型对比损失，将客户端漂移分解为特征漂移与原型漂移进行缓解。此外，设计归一化影响聚合策略，根据客户端对全局表征的贡献自适应加权原型，降低偏差或低质量更新的影响。在公开医疗数据集HAM10000和Matek-19上的实验表明，FedProIn在非独立同分布条件下分别取得81.1%和95.8%的准确率，优于现有基线方法。代码已开源。
原文摘要 · Abstract (English)
Federated learning (FL) is severely hindered by statistical heterogeneity due to variations in scanners, acquisition protocols, and patient populations. Such non-IID data induces client drift during local optimization, leading to unstable convergence and suboptimal global models when parameter-based aggregation is applied. We propose a prototype-based, influence-aware federated learning framework (FedProIn) that uses multiple learnable class prototypes to capture shared semantic structures across heterogeneous clients. We introduce feature divergence loss and prototype contrastive loss to mitigate client drift by decomposing it into feature drift and prototype drift. In addition, we propose a normalized influence aggregation strategy that adaptively weights client prototypes according to their contribution to the global representation, reducing the impact of biased or low-quality updates. Experimental results on two publicly available medical datasets, HAM10000 and Matek-19, demonstrate that FedProIn achieves accuracies of (83.5% IID, 81.1% non-IID) on HAM10000 and (96.2% IID, 95.8% non-IID) on Matek-19, respectively, outperforming existing baselines in both conditions. Our code is available at https://github.com/harsh-kmr/FedProIn.
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