用双注意力头提升心电图分类的联邦学习个性化效果
Dual Attention Heads for Personalized Federated Learning in ECG Classification
- 将Transformer注意力头分为全局与本地两支，分别捕捉共性与机构特异性特征
- 在FedCVD数据集上性能优于现有联邦学习方法，尤其在不同机构间表现更稳定
- 揭示了个性化程度需根据机构差异调整，为实际部署提供依据
联邦学习（FL）可在不共享敏感患者数据的前提下实现跨机构协作建模。然而，不同医疗机构间心电图（ECG）数据存在显著异质性，给鲁棒分类带来挑战。本文提出FedDualAtt，一种个性化联邦学习方法，将Transformer注意力头拆分为全局和本地分支：全局头通过FedAvg聚合以捕捉跨站点共性模式，本地头保持客户端专属以适应各机构的记录特征。在心血管疾病检测的联邦学习基准数据集FedCVD上的实验表明，FedDualAtt在心电图分类任务中优于现有联邦学习及个性化联邦学习方法。对全局-本地头比例的分析显示，不同客户端受益于不同程度的架构个性化。
原文摘要 · Abstract (English)
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data. However, the inherent heterogeneity of electrocardiogram (ECG) data across healthcare providers presents significant technical challenges for robust classification. We propose FedDualAtt, a personalized federated learning approach that splits transformer attention heads into global and local branches. Global heads are aggregated via FedAvg to capture shared cross-site patterns, while local heads remain client-specific to adapt to institution-level recording characteristics. Experiments on FedCVD, an FL benchmark for cardiovascular disease detection, demonstrate that FedDualAtt outperforms existing FL and personalized FL methods in ECG classification tasks. Analysis of global-local head ratios reveals that different clients benefit from varying levels of architectural personalization.
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