Abstract
In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) system and a set of users powered by independent energy harvesting sources to cooperatively perform FL. Since a certain number of users may not be served due to interference and energy constraints, a joint energy management and user scheduling problem is considered. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To determine the effect of various wireless factors (transmit power and number of scheduled users) on training loss, the convergence rate of the FL algorithm is analyzed. Given this analytical result, the original user scheduling and energy management optimization problem can be decomposed, simplified and solved. Simulation results show that the proposed algorithm can reduce training loss compared to a standard FL algorithm.
ACKNOWLEDGMENT This research was sponsored in part by the TÜBiTAK-QNRF Joint Funding Program grant (AICC03-0324-200005) from the Scientific and Technological Research Council of Turkey and Qatar National Research Fund (QNRF) and in part by the U.S. National Science Foundation under Grant CCF-1908308. The findings achieved herein are solely the responsibility of the authors.
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