2024
Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives
ICASSP 2024accepted
This paper proposes a locally differentially private federated learning algorithm for strongly convex but possibly nonsmooth problems that protects the gradients of each worker against an honest but curious server. The proposed algorithm adds artificial noise to the shared information to ensure priv…