Mobility in Hierarchical Federated Learning: Resource Allocation and Convergence Analysis

Kecheng Fan, Wen Chen, Jun Li, Qingqing Wu, Ming Ding, Xuefeng Han, Kang Wei, Xiumei Deng

Abstract: The proliferation of fifth-generation (5G) communication technologies and the Internet of Things (IoT) has led to massive distributed datasets across numerous user devices. Traditional centralized machine learning methods suffer from large communication overhead and damage of data privacy. Federated Learning (FL) offers a decentralized approach, allowing collaborative local model training and global aggregation, thus preserving data privacy and reducing communication costs. However, implementing FL in wireless networks is complicated due to limited wireless resources, increasing model complexity, and user mobility. In this paper, we address the impact of user mobility in hierarchical federated learning (HFL) systems and propose a resource allocation and user scheduling strategy to minimize energy consumption while maintaining learning performance. We design a comprehensive model that considers user mobility, wireless communication, and computing resources. Using the Lyapunov optimization method, we transform the long-term optimization problem into manageable subproblems, enabling efficient resource allocation and user selection. Our proposed Low Cost Scheduling Algorithm (LCSA) achieves an $\boldsymbol {O}\left ({{{}\frac {1}{N}}}\right)$ convergence rate, balancing local-edge divergence and improving overall convergence. Experimental results testify that our algorithm significantly reduces energy consumption while achieving high test accuracy compared to baseline methods, highlighting the positive effects of mobility on system performance.

External IDs:dblp:journals/tccn/FanCLWDHWD26