Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
This addresses performance degradation in federated learning for wireless networks with heterogeneous data and communication, but it is incremental as it builds on existing clustering approaches.
The paper tackles the problem of non-IID data and communication heterogeneity degrading federated learning in wireless environments by proposing a dual-segment clustering strategy, which improves convergence rate and achieves superior accuracy compared to classical methods.
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.