Delay Minimization for Federated Learning Over Wireless Communication Networks
This addresses delay issues for federated learning in wireless communication networks, but it is incremental as it builds on existing FL frameworks.
The paper tackled minimizing delay in federated learning over wireless networks by jointly optimizing computation and communication latencies, achieving up to 27.3% reduction in delay compared to conventional methods.
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.