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Federated learning (FL) is a machine learning model that preserves dataprivacy in the training process. Specifically, FL brings the model directly tothe user equipments (UEs) for local training, where an edge server periodicallycollects the trained parameters to produce an improved model and sends it backto the UEs. However, since communication usually occurs through a limitedspectrum, only a portion of the UEs can update their parameters upon eachglobal aggregation. As such, new scheduling algorithms have to be engineered tofacilitate the full implementation of FL. In this paper, based on a metrictermed the age of update (AoU), we propose a scheduling policy by jointlyaccounting for the staleness of the received parameters and the instantaneouschannel qualities to improve the running efficiency of FL. The proposedalgorithm has low complexity and its effectiveness is demonstrated by MonteCarol simulations.

Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks