Federated Calibration and Evaluation of Binary Classifiers

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Abstract

We address two major obstacles to practical deployment of AI-based models on distributed private data. Whether a model was trained by a federation of cooperating clients or trained centrally, (1) the output scores must be calibrated, and (2) performance metrics must be evaluated — all without assembling labels in one place. In particular, we show how to perform calibration and compute the standard metrics of precision, recall, accuracy and ROC-AUC in the federated setting under three privacy models (𝑖) secure aggregation, (𝑖𝑖) distributed differential privacy, (𝑖𝑖𝑖) local differential privacy. Our theorems and experiments clarify tradeoffs between privacy, accuracy, and data efficiency. They also help decide if a given application has sufficient data to support federated calibration and evaluation.