In the healthcare industry, Federated learning facilitates remarkable prospects owing to its property of data privacy preservation. Federated learning expedites distributed collaborative paradigm in healthcare systems in order to perform artificial intelligence training by coordinating multiple clients such as hospitals, clinical institutions, insurance companies, and pharmaceutical industries without exchanging the raw data.
In the healthcare and biomedical domain, federated learning is utilized in several applications, for instance, wearable healthcare devices, Electronic Health Records (EHRs) administration, Internet of Medical Things (IoMT), patient mortality prediction, remote health monitoring, medical imaging, disease diagnosis, clinical natural language processing, health decision based on clinical data, collaborative drug discovery and smart healthcare. Various surveys regarding federated learning in the medical field explore the application areas, impacts, requirements, real-world implementations, advanced frameworks, obscurities, and future directions.
Some of the notable impediments that need to address in the federated learning-based healthcare systems are data heterogeneity traceability, accountability, enhancements in privacy and security guarantee, quality of data, and system architecture. Thus, federated learning in the health care domain expects more research efforts in forthcoming years by indulging distributed healthcare enactments with privacy awareness.