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Secure Federated Learning for Cognitive Radio Sensing

Signal Processing 2023-04-14 v1 Artificial Intelligence Machine Learning Systems and Control Systems and Control
Authors: Malgorzata Wasilewska , Hanna Bogucka , H. Vincent Poor
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Abstract

This paper considers reliable and secure Spectrum Sensing (SS) based on Federated Learning (FL) in the Cognitive Radio (CR) environment. Motivation, architectures, and algorithms of FL in SS are discussed. Security and privacy threats on these algorithms are overviewed, along with possible countermeasures to such attacks. Some illustrative examples are also provided, with design recommendations for FL-based SS in future CRs.

Keywords

radio frequency signal processing

Cite

@article{arxiv.2304.06519,
  title  = {Secure Federated Learning for Cognitive Radio Sensing},
  author = {Malgorzata Wasilewska and Hanna Bogucka and H. Vincent Poor},
  journal= {arXiv preprint arXiv:2304.06519},
  year   = {2023}
}

Comments

7 pages, 6 figures

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