# Secure Federated Learning for Cognitive Radio Sensing

arXiv: https://arxiv.org/abs/2304.06519 · HTML: https://mlsanity.com/paper/2304.06519/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 23 Mar 2023.

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

- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning](https://mlsanity.com/paper/1804.00308/) (2018)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [On Safeguarding Privacy and Security in the Framework of Federated Learning](https://mlsanity.com/paper/1909.06512/) (2019)
- [When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges](https://mlsanity.com/paper/2201.04677/) (2022)
- [Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors](https://mlsanity.com/paper/2202.00137/) (2022)

## Built on by (most cited first, 4 of 4)

- [Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems](https://mlsanity.com/paper/2406.01727/) (2024)
- [Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing](https://mlsanity.com/paper/2406.12330/) (2024)
- [Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks](https://mlsanity.com/paper/2507.12127/) (2025)
- [Mitigating Evasion Attacks in Fog Computing Resource Provisioning Through Proactive Hardening](https://mlsanity.com/paper/2603.25257/) (2026)

## Abstract (arXiv)

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.