Journal
IEEE NETWORK
Volume 36, Issue 5, Pages 106-112Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.001.2200202
Keywords
Training; Data privacy; Satellites; Federated learning; Data integration; Computer architecture; Security
Categories
Funding
- National Key Research and Development Program of China [2020YFB1804800]
- National Natural Science Foundation of China [62222101]
- Young Elite Scientist Sponsorship Program by CAST [2020QNRC001]
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Satellite communication combined with mobile edge computing in satellite MEC can address data privacy issues and improve system efficiency.
Satellite communication has made great progress in recent years since it is characterized by wide information coverage and can support diverse types of users, which beneficially fulfills the demand of beyond 5G communications. Besides, mobile edge computing (MEC) technologies energize the edge devices with computational abilities to deal with the majority of training tasks without having to upload to the cloud server, which substantially enhances a system's efficiency. In satellite MEC, the raw data of edge users vested in different owners cannot be allowed to be shared, considering data privacy requirements. To address this, federated learning (FL) architecture can be applied to satellite MEC where only parameters and model updates can be transmitted, which avoids the interaction of raw data from diverse sources. In this article, we construct a FL-based satellite MEC architecture, followed by introducing its key techniques in the aspects of resource management and multi-modal data fusion. Furthermore, we study the data privacy and security protection on the FL-aided satellite MEC relying on a blockchain framework. Finally, we portray the challenges of FL-aided satellite MEC systems.
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