Joint User Cooperation and Scheduling for Federated Learning in CFmMIMO Networks

Abstract

This paper proposes a joint user cooperation and scheduling (JUCS) method to minimize the maximum transmission time among users in the synchronous federated learning (FL) architecture, since the user with the worst quality link limits the training performance of FL. To this end, an optimization problem is formulated to find the optimal user cooperation pairing and the power control coefficients. Meanwhile, as the links among users operate on the same frequency bands, the interface between them is taken into account in this paper. As the problem is non-convex, we decouple it into two sub-problems. For the first sub-problem, we propose a fair scheduling (FS)-based algorithm to schedule the helping users to pair with the ones who are required to be assisted. For the second sub-problem, an algorithm based on deterministic policy gradient (DDPG) is proposed to find the optimized power control coefficients. Experiments show that the proposed JUCS method shortens about 34.48% transmission time of the worst user in FL compared to the traditional one without JUCS.