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Federated learning (FL) leverages data distributed at the edge of the networkto enable intelligent applications. The efficiency of FL can be improved byusing over-the-air computation (AirComp) technology in the process of gradientaggregation. In this paper, we propose a relay-assisted large-scale FLframework, and investigate the device scheduling problem in relay-assisted FLsystems under the constraints of power consumption and mean squared error(MSE). we formulate a joint device scheduling, and power allocation problem tomaximize the number of scheduled devices. We solve the resultant non-convexoptimization problem by transforming the optimization problem into multiplesparse optimization problems. By the proposed device scheduling algorithm,these sparse sub-problems are solved and the maximum number of federatedlearning edge devices is obtained. The simulation results demonstrate theeffectiveness of the proposed scheme as compared with other benchmark schemes.

Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning