Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments

TL;DR

This paper introduces a dual-segment clustering strategy for hierarchical federated learning that effectively manages data and communication heterogeneity, improving convergence and accuracy in wireless environments.

Contribution

The paper proposes a novel DSC strategy using SNR and information matrices with affinity propagation to enhance client clustering in heterogeneous wireless FL.

Findings

Improves convergence rate of wireless FL.

Achieves higher accuracy in heterogeneous environments.

Outperforms classical clustering methods.

Abstract

Non-independent and identically distributed (Non- IID) data adversely affects federated learning (FL) while heterogeneity in communication quality can undermine the reliability of model parameter transmission, potentially degrading wireless FL convergence. This paper proposes a novel dual-segment clustering (DSC) strategy that jointly addresses communication and data heterogeneity in FL. This is achieved by defining a new signal-to-noise ratio (SNR) matrix and information quantity matrix to capture the communication and data heterogeneity, respectively. The celebrated affinity propagation algorithm is leveraged to iteratively refine the clustering of clients based on the newly defined matrices effectively enhancing model aggregation in heterogeneous environments. The convergence analysis and experimental results show that the DSC strategy can improve the convergence rate of wireless FL and demonstrate superior accuracy in heterogeneous environments compared to classical clustering methods.