Secure Federated Learning for Cognitive Radio Sensing

Małgorzata Wasilewska; Hanna Bogucka; H. Vincent Poor

doi:10.1109/MCOM.001.2200465

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Title

Secure Federated Learning for Cognitive Radio Sensing

Authors

Małgorzata Wasilewska (WIiT) ^{[ 1 ][ 2.3 ][ P ]}
Hanna Bogucka (WIiT) ^{[ 1 ][ 2.3 ][ P ]}
H. Vincent Poor

^{[ 1 ]} Instytut Radiokomunikacji, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | ^{[ P ]} employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2023

Published in

IEEE Communications Magazine

Journal year: 2023 | Journal volume: vol. 61 | Journal number: iss. 3

Article type

scientific article

Publication language

english

Keywords

EN

spectrum sensing
federated learning
security
cognitive radio

Abstract

EN 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.

Pages (from - to)

68 - 73

DOI

10.1109/MCOM.001.2200465

URL

https://ieeexplore.ieee.org/document/10080880

Ministry points / journal

200

Impact Factor

8,3

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