Federated learning for national healthcare systems: Balancing privacy and innovation

Abstract

Federated Learning provides a revolutionary model for handling the United States' healthcare data while addressing privacy concerns and pushing forward innovations. FL works especially in contrast to other organizational top-down approaches that do not allow for decentralization while training AI over various datasets—patient data, for instance, are considerably sensitive. This shift is important in creating a new generation of AI solutions for healthcare that can be used to prevent deaths, improve care, and cut on expenses. Thus, when applied to healthcare, Federated Learning can help unleash the value of massive and varied datasets while remaining compliant with privacy laws. This paper discusses how FL can be implemented in the healthcare systems of different nations and how this has the potential to greatly enhance medical research, pharmaceuticals, and disease prevention. Most notable, the article describes the concrete obstacles of data heterogeneity, model accuracy, and the ethical implications of FL at scale. The outcomes of this research bring to light FL as a crucial element in how innovation can be effected without infringing the rights of patients by enhancing the capacity for using efficient delivery of healthcare in the country.