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This research introduces GrAUC-PFL, a federated learning framework that directly optimizes the Area Under the Curve (AUC) metric while preserving patient data privacy across geographically distributed healthcare institutions. By employing graph-based regularization, the method ensures model similarity between neighboring entities with comparable data distributions, addressing the challenge of institutional heterogeneity without centralizing sensitive information. The approach demonstrates enhanced discriminative performance for diagnostic and risk-prediction models in scenarios where local data sharing is restricted by governance policies.
Geographically Regularized AUC-Maximizing Personalized Federated Learning
arXiv:2609.08379v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2609.08379 [cs.LG]
(or arXiv:2609.08379v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.08379 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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