Hierarchical Federated Learning Across Heterogeneous Cellular Networks (1909.02362v1)

Abstract: We study collaborative 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.

Hierarchical Federated Learning Across Heterogeneous Cellular Networks

In this paper, the authors explore a hierarchical federated learning (FL) framework applied to heterogeneous cellular networks (HCNs), presenting a novel approach to enhancing communication efficiency. Centralized ML typically demands offloading substantial datasets to edge or cloud servers, which is often impractical in wireless networks due to constraints involving latency, bandwidth, and privacy. Federated edge learning (FEEL) offers an alternative by facilitating ML training directly at edge devices, transmitting only model updates rather than raw data.

The proposed hierarchical framework introduces a two-layered structure comprised of small cell base stations (SBSs) and a macro base station (MBS). Each SBS coordinates FL among mobile users (MUs) within its cell, while model updates are periodically exchanged with the MBS to achieve global consensus. This architecture is tailored to optimize communication resources by employing gradient sparsification and periodic averaging. These techniques decrease communication latency significantly without compromising model accuracy, as demonstrated using the CIFAR-10 dataset for benchmark testing.

Key Elements and Results

• Hierarchical FL Architecture: The paper leverages SBSs to manage local models and reduces communication burdens by limiting data exchange to lower tiers, while still achieving global aggregation at regular intervals. This method reduces distance-related transmission inefficiencies common in traditional FL approaches where MUs communicate directly with the MBS.
• Communication Efficiency: By implementing gradient sparsification, the framework minimizes the transmitted data size, thus tackling the inherent issue of extensive communication overhead present in FL. This enhancement is particularly vital when scaling the framework to accommodate numerous devices typical in HCNs.
• Latency Analysis: The researchers provide a thorough end-to-end latency model, capturing both uplink and downlink dynamics between MUs, SBSs, and the MBS. The latency results indicate substantial reductions compared to conventional FL frameworks, especially as the path-loss exponent increases, where traditional centralized communication models suffer most.
• Numerical Evaluation: The experimental results highlight the efficacy of the proposed HFL framework in maintaining model accuracy while accelerating communication. The leaderboard accuracy for a ResNet18 model on the CIFAR-10 dataset, trained through hierarchical FL, surpasses the traditional FL counterpart, showcasing the benefits of structuring data exchanges in a hierarchical manner.

Implications and Future Directions

The hierarchical federated learning framework has several practical and theoretical implications. By exploiting the hierarchical network structure, the proposed approach adeptly addresses challenges like communication latency and efficient resource utilization. This contribution is particularly relevant to modern wireless networks where device heterogeneity and stringent resource allocation are critical concerns.

For future research, incorporating more sophisticated synchronization mechanisms and dynamic network adjustments could further enhance scalability. Exploring adaptive clustering strategies based on the network density and the varying data distribution or workload might yield further performance improvements. Moreover, addressing non-IID data distributions across devices remains an open challenge and an area of active interest for advancing FL methodologies.

Overall, this paper provides a substantial framework adaptable to various network scenarios, setting the stage for strengthened implementations of federated learning across broadly varying infrastructures.