The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE) applications and future networks (e.g., sixth-generation (6G) networks) has raised a number of operational challenges and limitations, for example in terms of security and privacy. Edge learning is an emerging approach to training models across distributed clients while ensuring data privacy. Such an approach when integrated in future network infrastructures (e.g., 6G) can potentially solve challenging problems such as resource management and behavior prediction. However, edge learning (including distributed deep learning) are known to be susceptible to tampering and manipulation. This survey article provides a holistic review of the extant literature focusing on edge learning-related vulnerabilities and defenses for 6G-enabled Internet of Things (IoT) systems. Existing machine learning approaches for 6G–IoT security and machine learning-associated threats are broadly categorized based on learning modes, namely: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G–IoT intelligence. We also provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, namely: backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a comparative summary of the state-of-the-art defense methods against edge learning-related vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed.

中文翻译：

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支持 6G 的物联网边缘学习：漏洞、数据集和防御的全面调查

第五代（5G）无线网络在万物互联（IoE）应用和未来网络（例如第六代（6G）网络）中的部署提出了许多运营挑战和限制，例如在安全方面和隐私。边缘学习是一种跨分布式客户端训练模型的新兴方法，同时确保数据隐私。当这种方法集成到未来的网络基础设施（例如，6G）中时，可以潜在地解决资源管理和行为预测等具有挑战性的问题。然而，众所周知，边缘学习（包括分布式深度学习）容易受到篡改和操纵。这篇调查文章对现有文献进行了全面回顾，重点关注支持 6G 的物联网 (IoT) 系统的边缘学习相关漏洞和防御。针对 6G-IoT 安全和机器学习相关威胁的现有机器学习方法根据学习模式大致分类，即：集中式、联合式和分布式。然后，我们概述了 6G-IoT 智能的新兴技术。我们还对机器学习攻击的现有研究进行了全面的调查，并将威胁模型分为八类，即：后门攻击、对抗样本、组合攻击、中毒攻击、女巫攻击、拜占庭攻击、推理攻击和丢弃攻击。此外，我们还提供了针对边缘学习相关漏洞的最先进防御方法的全面而详细的分类和比较总结。最后，随着新的攻击和防御技术的实现，讨论了支持 6G 的物联网的新研究和未来总体前景。