Advancements in wearable medical devices in IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), we are witnessing how efficient healthcare services are provided to patients and how healthcare professionals are effectively used AI-based models to analyze the data collected from IoHT devices for the treatment of various diseases. To avoid privacy breaches, these data must be processed and analyzed in compliance with the legal rules and regulations such as HIPAA and GDPR. Federated learning is a machine leaning based approach that allows multiple entities to collaboratively train a ML model without sharing their data. This is particularly useful in the healthcare domain where data privacy and security are big concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy Enhancing Technologies (PETs) are a set of tools and techniques that are designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users' personal information and sensitive data from unauthorized access and tracking. This paper reviews PETs in detail and comprehensively in relation to FL in the IoHT setting and identifies several key challenges for future research.

翻译：物联网技术中可穿戴医疗设备的进步正在塑造现代医疗系统。随着医疗物联网（IoHT）的出现，我们见证了如何为患者提供高效的医疗服务，以及医疗专业人员如何有效利用基于AI的模型分析从IoHT设备收集的数据，用于治疗各种疾病。为避免隐私泄露，这些数据的处理和分析必须符合HIPAA和GDPR等法律法规。联邦学习是一种基于机器学习的方法，允许多个实体在不共享数据的情况下协作训练机器学习模型。这在数据隐私和安全备受关注的医疗领域尤为有用。尽管联邦学习解决了一些隐私问题，但目前仍缺乏对IoHT数据隐私保证的正式证明。隐私增强技术（PETs）是一套旨在增强在线通信和数据共享中隐私与安全的工具与技术，提供多种功能以保护用户的个人信息和敏感数据免遭未经授权的访问和追踪。本文在IoHT环境中详细且全面地回顾了与联邦学习相关的隐私增强技术，并指出了未来研究的若干关键挑战。