  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.
