  In this paper, the deployment of federated learning (FL) is investigated in
an energy harvesting wireless network in which the base station (BS) employs
massive multiple-input multiple-output (MIMO) to serve a set of users powered
by independent energy harvesting sources. Since a certain number of users may
not be able to participate in FL due to the interference and energy
constraints, a joint energy management and user scheduling problem in FL over
wireless systems is formulated. This problem is formulated as an optimization
problem whose goal is to minimize the FL training loss via optimizing user
scheduling. To find how the factors such as transmit power and number of
scheduled users affect the training loss, the convergence rate of the FL
algorithm is first analyzed. Given this analytical result, the user scheduling
and energy management optimization problem can be decomposed, simplified, and
solved. Further, the system model is extended by considering multiple BSs.
Hence, a joint user association and scheduling problem in FL over wireless
systems is studied. The optimal user association problem is solved using the
branch-and-bound technique. Simulation results show that the proposed user
scheduling and user association algorithm can reduce training loss compared to
a standard FL algorithm.
