Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing

TL;DR

This paper reviews the security and privacy challenges in 6G spectrum sharing using federated learning, highlighting potential threats and future defense strategies for protecting spectrum sensing data.

Contribution

It provides a comprehensive analysis of security and privacy issues in FL-enabled 6G spectrum sharing, including attack vectors and future research directions.

Findings

Identifies potential AI-powered security threats in 6G spectrum sharing

Analyzes privacy risks in federated spectrum sensing

Outlines future defense challenges and guidelines

Abstract

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.