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

arXiv: https://arxiv.org/abs/2406.12330 · HTML: https://mlsanity.com/paper/2406.12330/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [SNAP: Efficient Extraction of Private Properties with Poisoning](https://mlsanity.com/paper/2208.12348/) (2022)
- [Spectrum Data Poisoning with Adversarial Deep Learning](https://mlsanity.com/paper/1901.09247/) (2019)
- [Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks](https://mlsanity.com/paper/2204.03027/) (2022)
- [Secure Federated Learning for Cognitive Radio Sensing](https://mlsanity.com/paper/2304.06519/) (2023)
- [DeepSweep: Parallel and Scalable Spectrum Sensing via Convolutional Neural Networks](https://mlsanity.com/paper/2401.04805/) (2024)

## Abstract (arXiv)

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.