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Hierarchical Federated Learning Across Heterogeneous Cellular Networks · Around

2019

Hierarchical Federated Learning Across Heterogeneous Cellular Networks

Abad, Mehdi Salehi Heydar, Ozfatura, Emre, Gunduz, Deniz et al.

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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.

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