Paper Info
Title
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks
Abstract
Implementing federated learning (FL) algorithms in wireless networks has garnered a wide range of attention. However, few works have considered the impact of user mobility on the learning performance. To fill this research gap, we develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks where the mobile users may roam across edge access points (APs), leading to incompletion of inconsistent FL training. We provide the convergence analysis of conventional HFL with user mobility. Our analysis proves that the learning performance of conventional HFL deteriorates drastically with highly-mobile users. And such a decline in the learning performance will be exacerbated with small number of participants and large data distribution divergences among users’ local data. To circumvent these issues, we propose a mobility-aware cluster federated learning (MACFL) algorithm by redesigning the access mechanism, local update rule, and model aggregation scheme. We also conduct experiments to evaluate the learning performance of conventional HFL, a cluster federated learning (CFL) with simple averaging, and our proposed MACFL. The results show that our MACFL can enhance the learning performance, especially for three different cases: ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> ) the case of users with non-independent and identically distributed (non-IID) data, ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula> ) the case of users with high mobility, and ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$iii$ </tex-math></inline-formula> ) the case with a small number of users.
Year
DOI
Venue
2022
10.1109/TWC.2022.3166386
IEEE Transactions on Wireless Communications
Keywords
DocType
Volume
Hierarchical federated learning,user mobility,data heterogeneity,convergence analysis
Journal
21
Issue
ISSN
Citations 
10
1536-1276
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Chenyuan Feng100.68
howard hua yang221632.06
Deshun Hu300.68
Zhiwei Zhao415620.63
Tony Q. S. Quek53621276.75
Geyong Min62089224.70