Abstract
In this work, we consider a federated learning model in a wireless system
with multiple base stations and inter-cell interference. We apply a
differential private scheme to transmit information from users to their
corresponding base station during the learning phase. We show the convergence
behavior of the learning process by deriving an upper bound on its optimality
gap. Furthermore, we define an optimization problem to reduce this upper bound
and the total privacy leakage. To find the locally optimal solutions of this
problem, we first propose an algorithm that schedules the resource blocks and
users. We then extend this scheme to reduce the total privacy leakage by
optimizing the differential privacy artificial noise. We apply the solutions of
these two procedures as parameters of a federated learning system. In this
setting, we assume that each user is equipped with a classifier. Moreover, the
communication cells are assumed to have mostly fewer resource blocks than
numbers of users. The simulation results show that our proposed scheduler
improves the average accuracy of the predictions compared with a random
scheduler. Furthermore, its extended version with noise optimizer significantly
reduces the amount of privacy leakage.