Differentially Private AUC Computation in Vertical Federated Learning. (arXiv:2205.12412v1 [cs.LG])

Federated learning has gained great attention recently as a privacy-enhancing
tool to jointly train a machine learning model by multiple parties. As a
sub-category, vertical federated learning (vFL) focuses on the scenario where
features and labels are split into different parties. The prior work on vFL has
mostly studied how to protect label privacy during model training. However,
model evaluation in vFL might also lead to potential leakage of private label
information. One mitigation strategy is to apply label differential …

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