Hierarchical Federated Learning Across Heterogeneous Cellular Networks

TL;DR

This paper proposes a hierarchical federated learning framework for heterogeneous cellular networks that reduces communication latency by using gradient sparsification and periodic averaging, enabling efficient collaborative ML on mobile devices.

Contribution

It introduces a novel hierarchical FL architecture tailored for HCNs, combining gradient sparsification and periodic averaging to enhance communication efficiency.

Findings

Significant reduction in communication latency achieved.

Model accuracy maintained despite communication optimizations.

Effective hierarchical FL demonstrated on CIFAR-10 dataset.

Abstract

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.