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.

翻译：频谱共享在6G无线通信中日益重要，它促进了未使用频谱空洞的动态接入。近年来，利用机器学习技术进行频谱空洞感知已成为一个重要趋势。在此背景下，联邦学习赋能的频谱感知技术获得了广泛关注，该技术能够在无需公开无线用户设备私有频谱感知信息的情况下构建聚合机器学习模型。然而，协作训练的完整性以及来自本地用户的频谱信息隐私在很大程度上仍未得到充分探索。本文首先审视了面向未来6G场景的联邦学习赋能频谱共享的最新进展。随后，识别了6G中的实际攻击向量，以阐明在这些场景下潜在的人工智能驱动的安全与隐私威胁。最后，本研究展望了未来方向，包括实际防御挑战与指导原则。