  In this paper, the problem of delay minimization for federated learning (FL)
over wireless communication networks is investigated. In the considered model,
each user exploits limited local computational resources to train a local FL
model with its collected data and, then, sends the trained FL model parameters
to a base station (BS) which aggregates the local FL models and broadcasts the
aggregated FL model back to all the users. Since FL involves learning model
exchanges between the users and the BS, both computation and communication
latencies are determined by the required learning accuracy level, which affects
the convergence rate of the FL algorithm. This joint learning and communication
problem is formulated as a delay minimization problem, where it is proved that
the objective function is a convex function of the learning accuracy. Then, a
bisection search algorithm is proposed to obtain the optimal solution.
Simulation results show that the proposed algorithm can reduce delay by up to
27.3% compared to conventional FL methods.
