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Smart Grid and Internet of Things. 7th EAI International Conference, SGIoT 2023, TaiChung, Taiwan, November 18-19, 2023, Proceedings

Research Article

A Novel Hierarchical Federated Edge Learning Framework in Satellite-Terrestrial Assisted Networks

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-55976-1_8,
        author={Xin-tong Pei and Jian-jun Zeng and Zhen-jang Zhang},
        title={A Novel Hierarchical Federated Edge Learning Framework in Satellite-Terrestrial Assisted Networks},
        proceedings={Smart Grid and Internet of Things. 7th EAI International Conference, SGIoT 2023, TaiChung, Taiwan, November 18-19, 2023, Proceedings},
        proceedings_a={SGIOT},
        year={2024},
        month={3},
        keywords={hierarchical federated learning LEO satellite network edge computing inter-satellite links},
        doi={10.1007/978-3-031-55976-1_8}
    }
    
  • Xin-tong Pei
    Jian-jun Zeng
    Zhen-jang Zhang
    Year: 2024
    A Novel Hierarchical Federated Edge Learning Framework in Satellite-Terrestrial Assisted Networks
    SGIOT
    Springer
    DOI: 10.1007/978-3-031-55976-1_8
Xin-tong Pei1, Jian-jun Zeng2, Zhen-jang Zhang1,*
  • 1: Key Laboratory of Communication and Information Systems, Beijing Jiaotong, University
  • 2: Beijing InchTek Technology
*Contact email: zhangzhenjiang@bjtu.edu.cn

Abstract

On-board federated learning based on dense Low Earth Orbit satellite constellations can meet the data privacy requirements of users in the coverage of non-terrestrial networks. However, traditional satellite-terrestrial assisted federated learning may encounter challenges due to limited satellite resources. To solve the problem, a satellite-terrestrial assisted hierarchical federated edge learning (STA-HFEL) framework is established in this paper. By leveraging well-endowed cloud servers for processing, inter-satellite links, predictability in satellite positioning, and partial aggregation, substantial reductions in training duration and communication costs are achieved. Furthermore, we define a problem within the STA-HFEL framework that involves optimizing the allocation of computation and communication resources for device users to attain overall cost minimization. To address this challenge, we introduce a resource allocation algorithm that operates effectively. Extensive performance evaluations demonstrate that the potential of STA-HFEL as a cost-efficient and privacy-preserving approach for machine learning tasks across distributed remote environments.

Keywords
hierarchical federated learning LEO satellite network edge computing inter-satellite links
Published
2024-03-15
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-55976-1_8
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