Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity

Federated learning (FL) has emerged as a popular methodology for distributing machine learning across wireless edge devices. In this work, we consider optimizing the tradeoff between model performance and resource utilization in FL, under 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 in terms of model convergence speed and network resource utilization when the minibatch size and the combiner weights are adjusted. Additionally, our method can reduce the number of uplink communication rounds required during the model training period to reach the same accuracy.