Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy

Understand

Federated Learning with client-level differential privacy (DP) provides a promising framework for collaboratively training models while rigorously protecting clients' privacy.

• However, classic approaches like DP-FedAvg struggle when clients have heterogeneous privacy requirements, as they must uniformly enforce the strictest privacy level across all clients, leading to excessive DP noise and significant degradation in model utility.
• Existing methods to improve the model utility in such heterogeneous privacy settings often assume a trusted server and are largely heuristic, resulting in suboptimal performance and lacking strong theoretical foundations.
• In this work, we address these challenges under a practical attack model where both clients and the server are honest-but-curious.