Federated Computation of ROC and PR Curves
This work proposes a method to approximate Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves in Federated Learning settings where data privacy is crucial. It achieves this by estimating prediction score quantiles across distributed clients using hierarchical histograms and differential privacy, providing theoretical guarantees on approximation error.