Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification

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%.

• The paper presents a theoretical framework analyzing how gradient sparsification, model staleness, and mobility-induced contacts affect AFL convergence.
• The paper introduces the Mobility-Aware Dynamic Sparsification (MADS) algorithm, which adjusts sparsity rates to balance communication overhead and update quality.
• The paper validates its approach with experiments on CIFAR-10 and Argoverse, showing an 8.76% boost in accuracy and a 9.46% reduction in displacement error.

Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification

The paper addresses a pertinent challenge in the current landscape of federated learning (FL)—the integration of asynchronous federated learning (AFL) with mobile devices featuring intermittent connectivity. Traditional FL in mobile settings often involves synchronous updates, yet the proposed paper suggests an AFL approach to better handle the dynamic nature of mobile devices, which are characterized by unstable and sporadic network connections due to mobility.

Key Contributions

1. Theoretical Model for AFL Convergence Analysis: The paper introduces a theoretical framework to analyze the interplay between gradient sparsification, model staleness, and mobility-induced contact patterns, evaluating their collective effects on the convergence of AFL. This depth of analysis is notably absent in many existing studies and provides deeper insights into the factors impacting AFL convergence in mobile environments.
2. Mobility-Aware Dynamic Sparsification (MADS) Algorithm: By developing the MADS algorithm, the authors provide a novel strategy for dynamically adjusting the sparsification rate based on contact time and model staleness. This approach ensures a balance between communication overhead and model update quality, with closed-form solutions indicating adjusted sparsification degrees under varying mobility conditions.
3. Experimental Validation: Leveraging datasets such as CIFAR-10 for image classification and Argoverse for trajectory prediction, the authors demonstrate the superior performance of the MADS algorithm. Notably, the algorithm increased accuracy by 8.76% on CIFAR-10 and reduced displacement error by 9.46% on Argoverse as compared to benchmark methods.

Implications and Future Directions

The paper's convergence analysis and MADS algorithm significantly advance understanding in the domain of AFL, specifically in managing the dual challenges of sparsification error and model staleness due to device mobility. The proposed solutions suggest practical enhancements for AFL implementations in dynamic environments such as vehicular networks, smart city applications, and beyond, where timely model updates are critical despite limited connectivity periods.

Future work can explore more refined models and algorithms considering varied mobility patterns across different application scenarios. As device capabilities and network infrastructures evolve, analyzing the impact of advanced communication technologies like 6G on AFL will be crucial. Comparative studies involving real-time bidding and task allocation in edge intelligence can augment current models, providing more holistic solutions in deploying AFL. Moreover, addressing the security and privacy concerns intrinsic to FL frameworks while ensuring efficiency in asynchronous settings remains an open research domain.

In conclusion, this paper provides substantial contributions to AFL research by enhancing our understanding of the vital components influencing convergence in mobile settings and introducing adaptive sparsification strategies to address these issues pragmatically.