Mitigating Edge Heterogeneity for Hierarchical Federated Learning: Prototype System and Convergence Analysis

Hierarchical federated learning (HFL) emerges as a promising solution for distributed systems in real-world IoT, compensating for the limitations of client and training data through multilevel aggregation. However, severe system heterogeneity, such as data heterogeneity, differentiated communication and computing power, can cause model drift and negatively impact system performance, posing challenges to existing HFL frameworks. To address this issue, we propose a mitigating edge heterogeneity method for HFL (MEH-Fed) in this paper. First, we design a new HFL framework, where the key idea is to introduce an initial update of the edge server side to reduce the impact of heterogeneity on system performance. Edge servers use synthetic datasets to guide the update direction of client models, facilitating model training and accelerating convergence. Then, we conduct a convergence analysis of the proposed method, establish a theoretical upper bound, and analyze the effects of key parameters. Finally, we experimentally evaluate the performance of the proposed MEH-Fed in terms of system performance and convergence rate. Experimental results validate that MEH-Fed achieves optimal performance in both accuracy and efficiency, establishing a new paradigm in HFL architecture design for heterogeneous IoT environments.