  Federated learning (FL) has emerged as a popular technique for distributing
machine learning across wireless edge devices. We examine FL under two salient
properties of contemporary networks: device-server communication delays and
device computation heterogeneity. Our proposed StoFedDelAv algorithm
incorporates a local-global model combiner into the FL synchronization step. We
theoretically characterize the convergence behavior of StoFedDelAv and obtain
the optimal combiner weights, which consider the global model delay and
expected local gradient error at each device. We then formulate a network-aware
optimization problem which tunes the minibatch sizes of the devices to jointly
minimize energy consumption and machine learning training loss, and solve the
non-convex problem through a series of convex approximations. Our simulations
reveal that StoFedDelAv outperforms the current art in FL, evidenced by the
obtained improvements in optimization objective.
