Research Article
Federated Learning-Based Cross-layer Security Design for Satellite Networks
@INPROCEEDINGS{10.1007/978-3-031-86196-3_6, author={Zhisheng Yin and Yonghong Liu and Nan Cheng and Linlin Liang and Wenbin Sun and Tom H. Luan}, title={Federated Learning-Based Cross-layer Security Design for Satellite Networks}, proceedings={Wireless and Satellite Systems. 14th EAI International Conference, WiSATS 2024, Harbin, China, August 23--25, 2024, Proceedings, Part I}, proceedings_a={WISATS}, year={2025}, month={3}, keywords={Satellite networks Federated learning Cross-layer security Unsupervised learning}, doi={10.1007/978-3-031-86196-3_6} }- Zhisheng Yin
Yonghong Liu
Nan Cheng
Linlin Liang
Wenbin Sun
Tom H. Luan
Year: 2025
Federated Learning-Based Cross-layer Security Design for Satellite Networks
WISATS
Springer
DOI: 10.1007/978-3-031-86196-3_6
Abstract
The extensive coverage of satellite networks robustly supports federated learning (FL) in multiple domains. This combination protects user privacy and enables extensive data training, with promising applications in remote healthcare, smart agriculture, and environmental monitoring. However, existing FL primarily focuses on data training and aggregation, with less attention given to the secure transmission of model data during upload and download processes. This paper explores cross-layer security in satellite networks, focusing on the physical and application layers. We propose a beamforming optimization scheme based on unsupervised neural network to guarantee secure transmissions without compromising FL training performance. Simulation results underscore the efficacy of our approach in securing physical layer transmissions and affirm its practicality in maintaining robust FL training outcomes.
