Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningUser IdentificationSimilar Papers 제목 키워드 기반
A Streamlit-based Artificial Intelligence Trust Platform for Next-Generation Wireless Networks
With the rapid development and integration of artificial intelligence (AI) methods in next-generation networks (NextG), AI algorithms have provided significant advantages for NextG in terms of frequency spectrum usage, b…
Model PoisoningSelf-LearningCeltibero: Robust Layered Aggregation for Federated Learning
Federated Learning (FL) is an innovative approach to distributed machine learning. While FL offers significant privacy advantages, it also faces security challenges, particularly from poisoning attacks where adversaries …
Federated LearningRobust Federated Learning Against Poisoning Attacks: A GAN-Based Defense Framework
Federated Learning (FL) enables collaborative model training across decentralized devices without sharing raw data, but it remains vulnerable to poisoning attacks that compromise model integrity. Existing defenses often …
Federated LearningGenerative Adversarial NetworkPrivacy PreservingSparsification Under Siege: Defending Against Poisoning Attacks in Communication-Efficient Federated Learning
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, yet it faces significant challenges in communication efficiency and vulnerability to poisoning attack…
Federated LearningAdversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning
This paper presents a game-theoretic framework to study the interactions of attack and defense for deep learning-based NextG signal classification. NextG systems such as the one envisioned for a massive number of IoT dev…