Dynamic Privacy Allocation for Locally Differentially Private Federated
  Learning with Composite Objectives
Jiaojiao Zhang Dominik Fay Mikael Johansson
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 privacy and
dynamically allocates the time-varying noise variance to minimize an upper
bound of the optimization error subject to a predefined privacy budget
constraint. This allows for an arbitrarily large but finite number of
iterations to achieve both privacy protection and utility up to a neighborhood
of the optimal solution, removing the need for tuning the number of iterations.
Numerical results show the superiority of the proposed algorithm over
state-of-the-art methods.