通过地理正则化提升传染病风险预测的联邦学习模型性能
Geographically Regularized AUC-Maximizing Personalized Federated Learning
- 基于图结构正则化，让相邻机构模型参数趋同
- 直接优化AUC指标，提升模型判别能力
- 适合数据分布差异大且需保护隐私的医疗场景
准确的诊断与风险预测模型对传染病暴发期间的临床决策至关重要。然而，隐私和治理要求可能限制医疗机构间患者级数据共享，且数据分布常存在差异。此外，AUC被广泛用于评估模型判别性能，推动其在模型开发中的直接优化。本文提出地理正则化AUC最大化个性化联邦学习（GrAUC-PFL），通过直接优化平滑的成对AUC代理目标，在保持患者级数据本地化的同时，学习个性化模型，并考虑机构异质性。基于图的正则化促使地理邻近机构具有相似的系数向量，同时保留个性化特性。模拟实验与真实数据应用表明，当地理邻近机构具有相似数据生成特征时，该方法显著提升了判别性能。
原文摘要 · Abstract (English)
Accurate diagnostic and risk-prediction models are important for supporting clinical decision-making during infectious disease outbreaks. However, privacy and governance requirements may restrict patient-level data sharing across healthcare institutions, and data distributions often vary. Moreover, AUC is widely used to evaluate discriminative performance, motivating its direct optimization in model development. We propose geographically regularized AUC-maximizing personalized federated learning (GrAUC-PFL), which directly optimizes a smooth pairwise AUC surrogate to learn personalized models while keeping patient-level data local and accounting for institutional heterogeneity. Graph-based regularization encourages geographically neighboring institutions to have similar coefficient vectors while retaining a personalized models. Simulations and a real-data application suggest improved discriminative performance, particularly when geographically neighboring institutions have similar data-generating characteristics.
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