Hierarchical Federated Learning Across Heterogeneous Cellular Networks
This addresses communication efficiency in federated learning for wireless networks, but it is incremental as it builds on existing methods with optimizations like gradient sparsification.
The paper tackles the problem of collaborative machine learning across wireless devices with latency, bandwidth, and privacy constraints by proposing a hierarchical federated learning framework across heterogeneous cellular networks, showing that it significantly reduces communication latency without sacrificing model accuracy on the CIFAR-10 dataset.
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.