The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including 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 side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed.

翻译：第五代（5G）无线网络的持续部署不断暴露出其作为万物互联（IoE）应用核心驱动力的原始设计局限。这些5G挑战促使全球致力于推动未来网络（如第六代（6G）网络）的发展，以高效支持从自动驾驶能力到元宇宙等复杂应用。边缘学习是一种在分布式客户端间训练模型同时保护数据隐私的新型强大方法。该方法有望嵌入包括6G在内的未来网络基础设施，以解决资源管理与行为预测等挑战性问题。本综述文章系统回顾了面向6G物联网边缘学习脆弱性与防御的最新研究。我们总结了现有关于三种学习模式（集中式、联邦式与分布式）下6G物联网安全机器学习及机器学习相关威胁的研究文献。继而概述了赋能6G物联网智能化的新兴技术。此外，我们全面梳理了针对机器学习的攻击研究现状，将威胁模型分为八类：后门攻击、对抗样本、组合攻击、投毒攻击、女巫攻击、拜占庭攻击、推理攻击和丢弃攻击。进一步地，我们提供了面向边缘学习脆弱性的最新防御方法的详尽分类体系与并列对比分析。最后，随着新型攻击与防御技术的实现，探讨了6G物联网的研究前景与未来发展方向。