# Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution

arXiv: https://arxiv.org/abs/2312.17164 · HTML: https://mlsanity.com/paper/2312.17164/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [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)
- [Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation](https://mlsanity.com/paper/2010.00753/) (2020)
- [Deep Learning for Wireless Communications](https://mlsanity.com/paper/2005.06068/) (2020)
- [A Secure Federated Learning Framework for 5G Networks](https://mlsanity.com/paper/2005.05752/) (2020)
- [Deep Learning for Launching and Mitigating Wireless Jamming Attacks](https://mlsanity.com/paper/1807.02567/) (2018)
- [Adversarial Machine Learning in Wireless Communications using RF Data: A Review](https://mlsanity.com/paper/2012.14392/) (2020)
- [IoT Network Security from the Perspective of Adversarial Deep Learning](https://mlsanity.com/paper/1906.00076/) (2019)
- [Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks](https://mlsanity.com/paper/1911.00500/) (2019)
- [When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing](https://mlsanity.com/paper/1905.01430/) (2019)
- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks](https://mlsanity.com/paper/2204.03027/) (2022)
- [Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks](https://mlsanity.com/paper/2212.11194/) (2022)
- [Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks](https://mlsanity.com/paper/2301.05250/) (2023)
- [Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2212.11778/) (2022)

## Built on by (most cited first, 2 of 2)

- [Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats](https://mlsanity.com/paper/2505.23706/) (2025)
- [Targeted Attacks and Defenses for Distributed Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2510.15109/) (2025)

## 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.