Advancing Healthcare Informatics for Empowering Privacy and Security through Federated Learning Paradigms

Abstract

This research paper explores the transformative potential of federated learning in healthcare informatics, focusing on its pivotal role in balancing advancements with privacy and security imperatives. In an era marked by exponential growth in healthcare data, federated learning emerges as a promising paradigm to enable collaborative model training without compromising the confidentiality of sensitive patient information. Through a decentralized approach, this paper elucidates the mechanisms of secure aggregation, differential privacy, and encryption protocols inherent in federated learning, emphasizing their significance in preserving data privacy. By dissecting real-world implementations and case studies, it underscores the practical applicability of federated learning while addressing ethical implications, regulatory considerations, and potential challenges. Ultimately, this paper advocates for the widespread integration of federated learning in healthcare informatics, positing it as a cornerstone in advancing medical research while ensuring robust privacy and security safeguards.