mlsanityData to 7 Oct 2026

Jamming Attacks on Federated Learning in Wireless Networks

arXiv 2201.05172 ↗First posted 13 Jan 2022cs.LG cs.NIcited by 6

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) offers a decentralized learning environment so that a group of clients can collaborate to train a global model at the server, while keeping their training data confidential. This paper studies how to launch over-the-air jamming attacks to disrupt the FL process when it is executed over a wireless network. As a wireless example, FL is applied to learn how to classify wireless signals collected by clients (spectrum sensors) at different locations (such as in cooperative sensing). An adversary can jam the transmissions for the local model updates from clients to the server (uplink attack), or the transmissions for the global model updates the server to clients (downlink attack), or both. Given a budget imposed on the number of clients that can be attacked per FL round, clients for the (uplink/downlink) attack are selected according to their local model accuracies that would be expected without an attack or ranked via spectrum observations. This novel attack is extended to general settings by accounting different processing speeds and attack success probabilities for clients. Compared to benchmark attack schemes, this attack approach degrades the FL performance significantly, thereby revealing new vulnerabilities of FL to jamming attacks in wireless networks.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 47 of its 65 references that are in the map. First posted on arXiv 13 Jan 2022.

Builds on

47 of 65 references.

References · 47

  1. 2019Advances and Open Problems in Federated Learningcited by 3,021
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2019Towards Federated Learning at Scale: System Designcited by 826
  4. 2019Federated Machine Learning: Concept and Applicationscited by 409
  5. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  6. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  8. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  9. 2020Threats to Federated Learning: A Surveycited by 171
  10. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  11. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  12. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
35 more
  1. 2018Adversarial Attacks on Deep-Learning Based Radio Signal Classificationcited by 47
  2. 2020A Secure Federated Learning Framework for 5G Networkscited by 32
  3. 2018Deep Learning for Launching and Mitigating Wireless Jamming Attackscited by 31
  4. 2019Evaluating Adversarial Evasion Attacks in the Context of Wireless Communicationscited by 28
  5. 2020Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifierscited by 28
  6. 2020Adversarial Machine Learning in Wireless Communications using RF Data: A Reviewcited by 25
  7. 2019Generative Adversarial Network for Wireless Signal Spoofingcited by 21
  8. 2019IoT Network Security from the Perspective of Adversarial Deep Learningcited by 20
  9. 2019Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attackscited by 20
  10. 2019Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systemscited by 19
  11. 2019The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detectioncited by 17
  12. 2020Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channelscited by 17
  13. 2019Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learningcited by 15
  14. 2020Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofingcited by 14
  15. 2019Spectrum Data Poisoning with Adversarial Deep Learningcited by 13
  16. 2020Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifierscited by 13
  17. 2019When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensingcited by 12
  18. 2020When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directionscited by 12
  19. 2021Adversarial Machine Learning for 5G Communications Securitycited by 12
  20. 2021Robust Adversarial Attacks Against DNN-Based Wireless Communication Systemscited by 12
  21. 2021Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Networkcited by 11
  22. 2020Adversarial Machine Learning based Partial-model Attack in IoTcited by 9
  23. 2020Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifierscited by 9
  24. 2020Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifierscited by 9
  25. 2021How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learningcited by 8
  26. 2019Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustnesscited by 7
  27. 2019Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-trainingcited by 7
  28. 2020How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacycited by 7
  29. 2021Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyondcited by 6
  30. 2021Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learningcited by 4
  31. 2021Adversarial Attacks against Deep Learning Based Power Control in Wireless Communicationscited by 4
  32. 2020Investigating a Spectral Deception Loss Metric for Training Machine Learning-based Evasion Attackscited by 3
  33. 2021Gradient-based Adversarial Deep Modulation Classification with Data-driven Subsamplingcited by 3
  34. 2021Autoencoder-based Communications with Reconfigurable Intelligent Surfacescited by 3
  35. 2022IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identificationcited by 3

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 2.

6 papers.

  1. 2022Federated Learning for Distributed Spectrum Sensing in NextG Communication Networkscited by 7
  2. 2022Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networkscited by 6
  3. 2024R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Modelscited by 5
  4. 2023Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networkscited by 4
  5. 2025Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threatscited by 1
  6. 2025Targeted Attacks and Defenses for Distributed Federated Learning in Vehicular Networkscited by 0