Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification
Journal:
arXiv
Published Date:
Jun 8, 2025
Abstract
Asynchronous Federated Learning (AFL) enables distributed model training
across multiple mobile devices, allowing each device to independently update
its local model without waiting for others. However, device mobility introduces
intermittent connectivity, which necessitates gradient sparsification and leads
to model staleness, jointly affecting AFL convergence. This paper develops a
theoretical model to characterize the interplay among sparsification, model
staleness and mobility-induced contact patterns, and their joint impact on AFL
convergence. Based on the analysis, we propose a mobility-aware dynamic
sparsification (MADS) algorithm that optimizes the sparsification degree based
on contact time and model staleness. Closed-form solutions are derived, showing
that under low-speed conditions, MADS increases the sparsification degree to
enhance convergence, while under high-speed conditions, it reduces the
sparsification degree to guarantee reliable uploads within limited contact
time. Experimental results validate the theoretical findings. Compared with the
state-of-the-art benchmarks, the MADS algorithm increases the image
classification accuracy on the CIFAR-10 dataset by 8.76% and reduces the
average displacement error in the Argoverse trajectory prediction dataset by
9.46%.
