"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning

TL;DR

This paper introduces privacy-preserving mechanisms to measure demographic performance disparities in federated learning, enabling model holders to identify biases without compromising individual privacy.

Contribution

It proposes novel locally differentially private methods for measuring performance disparities across groups in federated learning, with theoretical error bounds.

Findings

Error decreases rapidly with more clients

Privacy does not significantly hinder disparity detection

Mechanisms are effective under realistic privacy budgets

Abstract

As in traditional machine learning models, models trained with federated learning may exhibit disparate performance across demographic groups. Model holders must identify these disparities to mitigate undue harm to the groups. However, measuring a model's performance in a group requires access to information about group membership which, for privacy reasons, often has limited availability. We propose novel locally differentially private mechanisms to measure differences in performance across groups while protecting the privacy of group membership. To analyze the effectiveness of the mechanisms, we bound their error in estimating a disparity when optimized for a given privacy budget. Our results show that the error rapidly decreases for realistic numbers of participating clients, demonstrating that, contrary to what prior work suggested, protecting privacy is not necessarily in conflict with identifying performance disparities of federated models.