Secure Federated Learning for Cognitive Radio Sensing
Secure Federated Learning for Cognitive Radio Sensing
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Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
Language
English
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Publication information
Publisher
Ithaca: Cornell University Library, arXiv.org
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Scope and Contents
Contents
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...
Alternative Titles
Full title
Secure Federated Learning for Cognitive Radio Sensing
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2801013731
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2801013731
Other Identifiers
E-ISSN
2331-8422
DOI
10.48550/arxiv.2304.06519
