Federated Learning (FL) in the Internet of Things (IoT) environments can enhance machine learning by utilising decentralised data, but at the same time, it might introduce significant privacy and security concerns due to the constrained nature of IoT devices. This represents a research challenge that we aim to address in this paper. We systematically analysed recent literature to identify privacy threats in FL within IoT environments, and evaluate the defensive measures that can be employed to mitigate these threats. Using a Systematic Literature Review (SLR) approach, we searched five publication databases (Scopus, IEEE Xplore, Wiley, ACM, and Science Direct), collating relevant papers published between 2017 and April 2024, a period which spans from the introduction of FL until now. Guided by the PRISMA protocol, we selected 49 papers to focus our systematic review on. We analysed these papers, paying special attention to the privacy threats and defensive measures -- specifically within the context of IoT -- using inclusion and exclusion criteria tailored to highlight recent advances and critical insights. We identified various privacy threats, including inference attacks, poisoning attacks, and eavesdropping, along with defensive measures such as Differential Privacy and Secure Multi-Party Computation. These defences were evaluated for their effectiveness in protecting privacy without compromising the functional integrity of FL in IoT settings. Our review underscores the necessity for robust and efficient privacy-preserving strategies tailored for IoT environments. Notably, there is a need for strategies against replay, evasion, and model stealing attacks. Exploring lightweight defensive measures and emerging technologies such as blockchain may help improve the privacy of FL in IoT, leading to the creation of FL models that can operate under variable network conditions.

翻译：物联网环境中的联邦学习能够利用分散数据增强机器学习能力，但同时也可能因物联网设备的受限特性引发严重的隐私与安全问题。这正是本文旨在应对的研究挑战。我们通过系统性文献分析，识别了物联网环境下联邦学习中的隐私威胁，并评估了可用于缓解这些威胁的防御措施。采用系统性文献综述方法，我们检索了五个文献数据库（Scopus、IEEE Xplore、Wiley、ACM和Science Direct），汇总了2017年至2024年4月期间发表的相关文献——这一时期涵盖了从联邦学习提出至今的发展历程。依据PRISMA协议，我们筛选出49篇文献作为系统性综述的核心研究对象。通过对这些文献的分析，我们特别关注了物联网特定语境下的隐私威胁与防御措施，并采用量身定制的纳入与排除标准以突显最新进展与关键见解。研究识别出多种隐私威胁，包括推理攻击、投毒攻击和窃听攻击，同时梳理了差分隐私和安全多方计算等防御措施。我们评估了这些防御措施在保护隐私的同时，是否会影响物联网环境下联邦学习功能完整性的有效性。本综述强调了为物联网环境量身定制强健高效隐私保护策略的必要性，尤其需要针对重放攻击、规避攻击和模型窃取攻击的策略。探索轻量级防御措施及区块链等新兴技术，或将有助于提升物联网联邦学习的隐私保护水平，从而构建出能在可变网络条件下稳定运行的联邦学习模型。