Federated learning (FL) enables distribution of machine workloads from the cloud to resource-limited edge devices. Unfortunately, current deep networks remain not only too compute-heavy for inference and training on devices, but also large communicating updates over bandwidth-constrained networks. In this paper, we develop, implement, experimentally validate a novel FL framework termed Dynamic ...