地理正则化的AUC最大化个性化联邦学习
Geographically Regularized AUC-Maximizing Personalized Federated Learning
浏览论文内容
中文总结 AI 辅助
提出地理正则化的AUC最大化个性化联邦学习（GrAUC-PFL），通过直接优化平滑成对AUC替代函数并利用图正则化促进邻近机构模型相似性，在保护隐私的同时提升判别性能。
中文摘要 AI 辅助
在传染病暴发期间，准确的诊断和风险预测模型对于支持临床决策至关重要。然而，隐私和治理要求可能限制患者级数据在医疗机构之间的共享，且数据分布往往存在差异。此外，AUC被广泛用于评估判别性能，这促使在模型开发中直接优化AUC。我们提出了地理正则化的AUC最大化个性化联邦学习（GrAUC-PFL），该方法直接优化一个平滑的成对AUC替代函数来学习个性化模型，同时保持患者级数据的本地性并考虑机构间的异质性。基于图的正则化鼓励地理上邻近的机构具有相似的系数向量，同时保留个性化模型。模拟实验和实际数据应用表明，该方法能提高判别性能，尤其是当地理上邻近的机构具有相似的数据生成特征时。
英文摘要
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.
发表机构
- Wakayama Medical University（和歌山医科大学）
- Doshisha University（同志社大学）
机构由 AI 辅助整理，请以论文原文为准。
