Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.

翻译：联邦学习是针对医院、临床研究实验室和移动设备等数据中心分布的数据集开发机器学习模型的过程,同时防止数据泄漏;这项调查审查了以前关于保健部门在各种使用案例和应用方面联合学习的研究。我们的调查表明,执业者在联邦学习主题方面应该了解哪些挑战、方法和应用。本文旨在阐述现有的研究,并列出保健行业联合学习的可能性。