  Federated learning (FL) is a machine learning model that preserves data
privacy in the training process. Specifically, FL brings the model directly to
the user equipments (UEs) for local training, where an edge server periodically
collects the trained parameters to produce an improved model and sends it back
to the UEs. However, since communication usually occurs through a limited
spectrum, only a portion of the UEs can update their parameters upon each
global aggregation. As such, new scheduling algorithms have to be engineered to
facilitate the full implementation of FL. In this paper, based on a metric
termed the age of update (AoU), we propose a scheduling policy by jointly
accounting for the staleness of the received parameters and the instantaneous
channel qualities to improve the running efficiency of FL. The proposed
algorithm has low complexity and its effectiveness is demonstrated by Monte
Carol simulations.
