In the medical field, the information from the clinical institutions, patient individuals, insurance companies, and pharmaceutical industries are highly confidential which are need to be processed and analyzed by the healthcare professionals for clinical diagnosis and maintenance. Health care data analytics helps the healthcare experts to analyze and access the health care data, which are disorganized due to the complex nature of the healthcare system.

Commonly used data analytics tools are deep learning and machine learning, but these models have the challenge of maintaining data privacy. Federated learning has emerged as the most promising tool to handle the data privacy issue, which is an essential part of the healthcare industry for protecting confidential patient information. Federated learning operates well on fragmented sensitive data by training a shared global model with a central aggregator server while preserving the data in local institutions.

Significant benefits of using federated learning in the healthcare industry are improved data privacy, the equitable trade-off between accuracy and utility, and low-cost health data training. Some of the application scenarios of federated learning in the healthcare industry are wearable healthcare devices, electronic health records, Internet of Medical Things(IoMT), patient mortality prediction, remote health monitoring, medical imaging, disease diagnosis, clinical natural language processing, and collaborative drug discovery. Challenges and future scopes of federated learning in healthcare are medical data stream, hybrid medical data partition, incentive mechanism, incorporating expert knowledge, and personalization.