Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives

The design This paper proposes a locally differentially private federated of DP algorithms in federated learning depends on the attack learning algorithm for strongly convex but possibly nonsmooth scenario and can be roughly divided into global DP and problems that protects the gradients of each worker local DP (LDP) [6, 7]. Global DP resists passive attackers against an honest but curious server. The proposed algorithm from outside the system and typically relies on the server to adds artificial noise to the shared information to ensure add noise to the aggregated information while the workers privacy and dynamically allocates the time-varying noise upload their true models or gradients, assuming that the upload variance to minimize an upper bound of the optimization communication channel is secure and the server is trustworthy error subject to a predefined privacy budget constraint.