mlsanityData to 14 Oct 2026

Secure Federated Learning for Cognitive Radio Sensing

arXiv 2304.06519 ↗First posted 23 Mar 2023eess.SP cs.AI cs.LGcited by 4

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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.

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

7 of 20 references.

References · 7

  1. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  2. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 225
  3. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  4. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  5. 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
  6. 2022When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challengescited by 6
  7. 2022Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensorscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systemscited by 1
  2. 2024Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharingcited by 0
  3. 2025Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networkscited by 0
  4. 2026Mitigating Evasion Attacks in Fog Computing Resource Provisioning Through Proactive Hardeningcited by 0