论文标题
智能医疗系统中的联合学习保存隐私保护：一项综合调查
Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
论文作者
论文摘要
电子设备和通信基础设施的最新进展已通过使用IOMT设备将传统的医疗系统彻底改变为智能医疗保健系统。但是，由于人工智能（AI）的集中培训方法，移动和可穿戴设备的使用引起了有关医院和最终用户之间已经传达的信息的隐私问题。 IOMT设备传达的信息是高度机密的，可以暴露于对手。在这方面，联邦学习（FL），分布式AI范式为IOMT的隐私保护提供了新的机会，而无需访问参与者的机密数据。此外，FL为最终用户提供隐私，因为在培训过程中仅共享梯度。对于FL的这些特定属性，在本文中，我们在IOMT中提出了与隐私相关的问题。之后，我们介绍了FL在IOMT网络中保存隐私保护的作用，并引入了一些高级FL建筑，其中包括深入加固学习（DRL），数字双胞胎和生成对抗性网络（GAN），以检测隐私威胁。随后，我们在智能医疗系统中提供了FL的一些实际机会。最后，我们通过为FL提供开放研究挑战来结束这项调查，该挑战可用于未来的智能医疗系统
Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centralized training approach of artificial intelligence (AI), the use of mobile and wearable IoMT devices raises privacy concerns with respect to the information that has been communicated between hospitals and end users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to end users as only gradients are shared during training. For these specific properties of FL, in this paper we present privacy related issues in IoMT. Afterwards, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Subsequently, we present some practical opportunities of FL in smart healthcare systems. At the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems
