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Efficient and Secure Aggregation Framework for Federated-Learning-Based Spectrum Sharing
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AI
DOI：10.1109/JIOT.2024.3357575.png)
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Abstract
En 中文
Spectrum-sharing technology is used to alleviate the tension and scarcity of spectrum resources, and federated learning can significantly enhance the performance of tasks, such as incumbent detection and improve the quality of spectrum sharing. However, spectrum-sharing methods based on federated learning still face challenges, such as large-scale data transmission and the lack of privacy protection for sensing nodes. To tackle these issues, in this article, we propose a compressed sensing (CS)-based transmission framework that integrates efficient aggregation and privacy protection. In particular, a multiple measurement vector (MMV)-CS model is used for efficient aggregation between the central server and sensing nodes. By designing different measurement vectors, local environment sensing nodes can be divided into different clusters, forming multiple superimposed transmission signals at the central server. Thus, the central server will obtain the aggregated information of local models from different clusters, completing the optimization of the global model in federated learning. In this process, the efficiency of data aggregation has been greatly improved, and the data privacy of individual environment sensing nodes is protected. The security analysis and simulation results are provided to validate the effectiveness of the proposed schemes. The detection performance of the proposed method is as good as that of the approach under the raw training samples, while the privacy-preserving and communication efficiency are significantly improved.
Keywords:
Compressed sensing (CS)
data privacy
federated learning
over-the-air computing
spectrum sharing
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W