Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution
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Abstract (arXiv)
This paper studies the poisoning attack and defense interactions in a federated learning (FL) system, specifically in the context of wireless signal classification using deep learning for next-generation (NextG) communications. FL collectively trains a global model without the need for clients to exchange their data samples. By leveraging geographically dispersed clients, the trained global model can be used for incumbent user identification, facilitating spectrum sharing. However, in this distributed learning system, the presence of malicious clients introduces the risk of poisoning the training data to manipulate the global model through falsified local model exchanges. To address this challenge, a proactive defense mechanism is employed in this paper to make informed decisions regarding the admission or rejection of clients participating in FL systems. Consequently, the attack-defense interactions are modeled as a game, centered around the underlying admission and poisoning decisions. First, performance bounds are established, encompassing the best and worst strategies for attackers and defenders. Subsequently, the attack and defense utilities are characterized within the Nash equilibrium, where no player can unilaterally improve its performance given the fixed strategies of others. The results offer insights into novel operational modes that safeguard FL systems against poisoning attacks by quantifying the performance of both attacks and defenses in the context of NextG communications.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 28 Dec 2023.
Builds on
17 of 24 references.
References · 17
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- 2020Adversarial Machine Learning in Wireless Communications using RF Data: A Reviewcited by 25
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- 2019Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attackscited by 20
- 2019When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensingcited by 12
5 more
- 2021Adversarial Machine Learning for 5G Communications Securitycited by 12
- 2022Federated Learning for Distributed Spectrum Sensing in NextG Communication Networkscited by 7
- 2022Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networkscited by 6
- 2023Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networkscited by 4
- 2022Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learningcited by 1
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