  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.
