Applications of Federated Learning in Healthcare—A New Paradigm for Digital Health

In recent times, a lot of development has taken place in deep learning, as a result of which development in smart healthcare applications has increased to a great extent. It successfully improved clinical institutions’ quality of care through data-driven insight. Strong data-drivenness underpins excellent deep learning models. The performance of such a model gets more reliable and generalizable as more data is trained on it. To train a model, the physiological information of the patient must be kept in a large storage area, which presents challenges related to privacy, ownership, and stringent regulations. Federated learning overcomes the aforementioned difficulties by using a shared central server as well as a deep learning model. The local party still has access to patient data, maintaining the security and anonymity of the information. In this study, we first give a thorough, current overview of the federated learning research that has been done in healthcare applications. We also discussed the difficulties, approaches, and applications of federated learning that a practitioner should be aware of.