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

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

URL: http://arxiv.org/abs/2405.09276v2
Date: Thu, 14 Nov 2024 08:06:46 GMT
Title: Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
Authors: Pengcheng Sun, Erwu Liu, Wei Ni, Kanglei Yu, Xinyu Qu, Rui Wang, Yanlong Bi, Chuanchun Zhang, Abbas Jamalipour,
Abstract summary: Non-independent and identically distributed (Non- IID) data adversely affects federated learning (FL) This paper proposes a novel dual-segment clustering (DSC) strategy that jointly addresses communication and data heterogeneity in FL. The convergence analysis and experimental results show that the DSC strategy can improve the convergence rate of wireless FL.
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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.