
Research Article
A Novel Hierarchical Federated Edge Learning Framework in Satellite-Terrestrial Assisted Networks
@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
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.