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Federated learning (FL) has gained significant attention recently as aprivacy-enhancing tool to jointly train a machine learning model by multipleparticipants. The prior work on FL has mostly studied how to protect labelprivacy during model training. However, model evaluation in FL might also leadto potential leakage of private label information. In this work, we propose anevaluation algorithm that can accurately compute the widely used AUC (areaunder the curve) metric when using the label differential privacy (DP) in FL.Through extensive experiments, we show our algorithms can compute accurate AUCscompared to the ground truth. The code is available at{\url{https://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC}}.

DPAUC: Differentially Private AUC Computation in Federated Learning