DPAUC: Differentially Private AUC Computation in Federated Learning. (arXiv:2208.12294v1 [cs.LG])

Federated learning (FL) has gained significant attention recently as a
privacy-enhancing tool to jointly train a machine learning model by multiple
participants. The prior work on FL has mostly studied how to protect label
privacy during model training. However, model evaluation in FL might also lead
to potential leakage of private label information. In this work, we propose an
evaluation algorithm that can accurately compute the widely used AUC (area
under the curve) metric when using the label differential privacy …

arxiv auc computation federated learning learning lg