Decentralized and Personalized Federated Learning

In this paper, we consider the personalized federated learning problem minimizing the average of strongly convex functions. We propose an approach which allows to solve the problem on a decentralized network by introducing a penalty function built upon a communication matrix of decentralized communications over a network and the application of the Sliding algorithm. The practical efficiency of the proposed approach is supported by the numerical experiments.