Towards Robust Federated Analytics via Differentially Private Measurements of Statistical Heterogeneity
This work addresses the pressing issue of maintaining accuracy in differentially private federated analytics when dealing with statistically heterogeneous datasets, which is incremental as it builds on existing methods for measuring heterogeneity.
The paper tackled the problem of accuracy loss in differentially private federated analytics due to statistical heterogeneity by developing an analytic mechanism to optimize privacy parameters, which delivered superior accuracy compared to classic mechanisms and centralized settings without significant accuracy loss for heterogeneous samples.
Statistical heterogeneity is a measure of how skewed the samples of a dataset are. It is a common problem in the study of differential privacy that the usage of a statistically heterogeneous dataset results in a significant loss of accuracy. In federated scenarios, statistical heterogeneity is more likely to happen, and so the above problem is even more pressing. We explore the three most promising ways to measure statistical heterogeneity and give formulae for their accuracy, while simultaneously incorporating differential privacy. We find the optimum privacy parameters via an analytic mechanism, which incorporates root finding methods. We validate the main theorems and related hypotheses experimentally, and test the robustness of the analytic mechanism to different heterogeneity levels. The analytic mechanism in a distributed setting delivers superior accuracy to all combinations involving the classic mechanism and/or the centralized setting. All measures of statistical heterogeneity do not lose significant accuracy when a heterogeneous sample is used.