  Spectrum sharing is increasingly vital in 6G wireless communication,
facilitating dynamic access to unused spectrum holes. Recently, there has been
a significant shift towards employing machine learning (ML) techniques for
sensing spectrum holes. In this context, federated learning (FL)-enabled
spectrum sensing technology has garnered wide attention, allowing for the
construction of an aggregated ML model without disclosing the private spectrum
sensing information of wireless user devices. However, the integrity of
collaborative training and the privacy of spectrum information from local users
have remained largely unexplored. This article first examines the latest
developments in FL-enabled spectrum sharing for prospective 6G scenarios. It
then identifies practical attack vectors in 6G to illustrate potential
AI-powered security and privacy threats in these contexts. Finally, the study
outlines future directions, including practical defense challenges and
guidelines.
