Near-optimal Differentially Private Client Selection in Federated Settings

Abstract

We develop an iterative differentially private algorithm for client selection in federated settings. We consider a federated network wherein clients coordinate with a central server to complete a task; however, the clients decide whether to participate or not at a time step based on their preferences -- local computation and probabilistic intent. The algorithm does not require client-to-client information exchange. The developed algorithm provides near-optimal values to the clients over long-term average participation with a certain differential privacy guarantee. Finally, we present the experimental results to check the algorithm's efficacy.

To appear in the proceedings of the 59th Annual Allerton Conference on Communication, Control, and Computing, September 2023, Monticello, Illinois, USA