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

Machine Learning