Hugging Face Trending Papers
Sep 8

Geographically Regularized AUC-Maximizing Personalized Federated Learning

The paper introduces Geographically Regularized AUC-Maximizing Personalized Federated Learning (GrAUC-PFL), a method that directly optimizes a smooth pairwise AUC surrogate to train personalized models while keeping patient data local. It incorporates graph-based regularization so that geographically neighboring institutions share similar coefficient vectors, thereby addressing institutional heterogeneity. Experiments on simulations and real data demonstrate improved discriminative performance, especially when neighboring institutions have similar data-generating characteristics.

Hugging Face Trending Papers
Jun 3

Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving

Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis. Federated learning (FL) has attracted growing attention as a promising framework for collaborative model development, as it allows multiple institutions to jointly train predictive models without directly sharing or centralizing raw data.