Geographically Regularized AUC-Maximizing Personalized Federated Learning
This work proposes 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.
Mayu Hiraishi, Kensuke Tanioka, Toshio Shimokawa
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