School of Information Technology, King's Own Institute, Sydney, NSW 2000, Australia.
School of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, Australia.
Int J Environ Res Public Health. 2023 Aug 7;20(15):6539. doi: 10.3390/ijerph20156539.
Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client's data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.
联邦学习（FL）提供了一种分布式机器学习系统，允许参与者使用本地数据进行训练，通过消除数据共享的需求来创建共享模型。在医疗保健系统中，FL 允许医疗物联网（MIoT）设备和电子健康记录（EHR）在不将患者数据发送到中央服务器的情况下进行本地训练。这使得可以根据所有参与者的数据集做出医疗保健决策和诊断，并简化其他医疗保健流程。就用户数据隐私而言，这项技术允许在无需与中央服务器共享本地数据的情况下进行协作训练。然而，FL 存在隐私挑战，因为模型更新在客户端和服务器之间共享，这可能会被用于重新生成客户端的数据，从而违反医疗保健等领域应用程序的隐私要求。在本文中，我们对文献进行了回顾，以分析针对医疗保健系统中的 FL 提出的现有隐私和安全增强方法。已经确定，该领域的研究侧重于七种技术：差分隐私、同态加密、区块链、分层方法、对等共享、边缘设备上的智能以及混合、混合和混合方法。讨论了每种技术的优缺点和权衡取舍，并确定了这些七种用于医疗保健 FL 系统的隐私增强技术的未来可能。
