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）应用关键驱动力的原始概念所存在的局限性。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，使其能够高效支持从自动驾驶能力到元宇宙等复杂应用。边缘学习是一种新兴且强大的方法，能够在保护数据隐私的同时跨分布式客户端训练模型。该方法预计将被嵌入包括6G在内的未来网络基础设施中，以解决资源管理和行为预测等难题。本综述文章全面回顾了针对6G物联网中边缘学习脆弱性与防御的最新研究成果。我们总结了现有关于面向6G物联网安全的机器学习研究以及三种不同学习模式（集中式、联邦式和分布式）下的机器学习相关威胁的综述。随后，我们概述了用于6G物联网智能化的新兴赋能技术。此外，我们对现有的机器学习攻击研究进行了全面综述，并将威胁模型分为八类，包括后门攻击、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。同时，我们提供了对最先进边缘学习脆弱性防御方法的全面且详细的分类以及并排比较。最后，随着新型攻击与防御技术的实现，我们讨论了面向6G物联网的新研究方向和未来总体前景。