Federated Learning: The much-needed intervention in Healthcare Informatics

Abstract

As a transformational approach to health informatics, federated learning (FL) enables collaborative data analysis without compromising patient privacy. In this conceptual paper, we explore FL as a critical attendance that supports modern healthcare in the presence of data silos, regulatory compliance, and security. These conclusive demonstrations of FL’s power to unite distributed datasets across institutions in a secure way to improve diagnostics, predictive analytics, and personalized medicine, provide a convincing demonstration for Big Data. This technology prevents privacy risks and catalyses innovation for medical research in genomic, radiological and predictive healthcare. The paper concludes with recommendations for scaling FL in healthcare to meet future data-driven demands.