University of Oulu

S. Liu, G. Yu, X. Chen and M. Bennis, "Joint User Association and Resource Allocation for Wireless Hierarchical Federated Learning With IID and Non-IID Data," in IEEE Transactions on Wireless Communications, vol. 21, no. 10, pp. 7852-7866, Oct. 2022, doi: 10.1109/TWC.2022.3162595

Joint user association and resource allocation for wireless hierarchical federated learning with IID and non-IID data

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Author: Liu, Shengli1; Yu, Guanding2; Chen, Xianfu3;
Organizations: 1School of Information and Electrical Engineer- ing, Zhejiang University City College, Hangzhou 310015, China
2College of Information Science and Elec- tronic Engineering, Zhejiang University, Hangzhou 310027, China
3VTT Technical Research Centre of Finland, 90570 Oulu, Finland
4Centre for Wireless Communication, University of Oulu, 90540 Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 4.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-12-20


In this work, hierarchical federated learning (HFL) over wireless multi-cell networks is proposed for large-scale model training while preserving data privacy. However, the imbalanced data distribution has a significant impact on the convergence rate and learning accuracy. In addition, a large learning latency is incurred due to the traffic load imbalance among base stations (BSs) and limited wireless resources. To cope with these challenges, we first provide an analysis of the model error and learning latency in wireless HFL. Then, joint user association and wireless resource allocation algorithms are investigated under independent identically distributed (IID) and non-IID training data, respectively. For the IID case, a learning latency aware strategy is designed to minimize the learning latency by optimizing user association and wireless resource allocation, where a mobile device selects the BS with the maximal uplink channel signal-to-noise ratio (SNR). For the non-IID case, the total data distribution distance and learning latency are jointly minimized to achieve the optimal user association and resource allocation. The results show that both data distribution and uplink channel SNR should be taken into consideration for user association in the non-IID case. Finally, the effectiveness of the proposed algorithms are demonstrated by the simulations.

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Series: IEEE transactions on wireless communications
ISSN: 1536-1276
ISSN-E: 1558-2248
ISSN-L: 1536-1276
Volume: 21
Issue: 10
Pages: 7852 - 7866
DOI: 10.1109/twc.2022.3162595
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: The work of Guanding Yu was supported by research grant under Grant GDNRC[2021]32. The work of Xianfu Chen was supported by the Zhejiang Laboratory Open Program under Grant 2021LC0AB06.
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