6G mmWave Security Advancements Through Federated Learning and Differential Privacy

IF 4.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ammar Kamal Abasi;Moayad Aloqaily;Mohsen Guizani
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Abstract

This paper presents a new framework that integrates Federated Learning (FL) with advanced privacy-preserving mechanisms to enhance the security of millimeter-wave (mmWave) beam prediction systems in 6G networks. By decentralizing model training, the framework safeguards sensitive user information while maintaining high model accuracy, effectively addressing privacy concerns inherent in centralized Machine learning (ML) methods. Adaptive noise augmentation and differential privacy principles are incorporated to mitigate vulnerabilities in FL systems, providing a robust defense against adversarial threats such as the Fast Gradient Sign Method (FGSM). Extensive experiments across diverse scenarios, including adversarial attacks, outdoor environments, and indoor settings, demonstrate a significant 17.45% average improvement in defense effectiveness, underscoring the framework’s ability to ensure data integrity, privacy, and performance reliability in dynamic 6G environments. By seamlessly integrating privacy protection with resilience against adversarial attacks, the proposed solution offers a comprehensive and scalable approach to secure mmWave communication systems. This work establishes a critical foundation for advancing secure 6G networks and sets a benchmark for future research in decentralized, privacy-aware machine learning systems.
通过联合学习和差异隐私实现 6G 毫米波安全进步
本文提出了一种将联邦学习(FL)与先进的隐私保护机制相结合的新框架,以增强6G网络中毫米波(mmWave)波束预测系统的安全性。通过分散模型训练,该框架在保持高模型准确性的同时保护敏感用户信息,有效地解决集中式机器学习(ML)方法固有的隐私问题。自适应噪声增强和差分隐私原则被纳入减轻FL系统中的漏洞,提供对对抗性威胁的强大防御,如快速梯度符号方法(FGSM)。在包括对抗性攻击、室外环境和室内设置在内的各种场景下进行的广泛实验表明,该框架的防御效率平均提高了17.45%,强调了该框架在动态6G环境中确保数据完整性、隐私性和性能可靠性的能力。通过将隐私保护与对抗攻击的弹性无缝集成,所提出的解决方案提供了一种全面且可扩展的方法来保护毫米波通信系统。这项工作为推进安全6G网络奠定了关键基础,并为未来去中心化、隐私感知的机器学习系统的研究设定了基准。
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来源期刊
IEEE Transactions on Network and Service Management
IEEE Transactions on Network and Service Management Computer Science-Computer Networks and Communications
CiteScore
9.30
自引率
15.10%
发文量
325
期刊介绍: IEEE Transactions on Network and Service Management will publish (online only) peerreviewed archival quality papers that advance the state-of-the-art and practical applications of network and service management. Theoretical research contributions (presenting new concepts and techniques) and applied contributions (reporting on experiences and experiments with actual systems) will be encouraged. These transactions will focus on the key technical issues related to: Management Models, Architectures and Frameworks; Service Provisioning, Reliability and Quality Assurance; Management Functions; Enabling Technologies; Information and Communication Models; Policies; Applications and Case Studies; Emerging Technologies and Standards.
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