  We study collaborative machine learning (ML) across wireless devices, each
with its own local dataset. Offloading these datasets to a cloud or an edge
server to implement powerful ML solutions is often not feasible due to latency,
bandwidth and privacy constraints. Instead, we consider federated edge learning
(FEEL), where the devices share local updates on the model parameters rather
than their datasets. We consider a heterogeneous cellular network (HCN), where
small cell base stations (SBSs) orchestrate FL among the mobile users (MUs)
within their cells, and periodically exchange model updates with the macro base
station (MBS) for global consensus. We employ gradient sparsification and
periodic averaging to increase the communication efficiency of this
hierarchical federated learning (FL) framework. We then show using CIFAR-10
dataset that the proposed hierarchical learning solution can significantly
reduce the communication latency without sacrificing the model accuracy.
