Federated Learning for Healthcare: Balancing Data Privacy and Model Accuracy

Abstract

Purpose: Federated Learning (FL) is transforming the way machine learning models are trained by allowing institutions to collaborate without sharing sensitive data. This is especially valuable in healthcare, where patient records are often stored separately across hospitals and research centers. This decentralized approach allows healthcare providers, researchers, and organizations to leverage collective intelligence from distributed datasets, leading to advancements in diagnostics, treatment personalization, and patient outcomes. Materials and Methods: However, the adoption of FL in healthcare is not without challenges, particularly in balancing the dual objectives of preserving data privacy and maintaining model accuracy. In this article, we explore how FL is being applied in healthcare, examining the balance between protecting patient privacy and ensuring high model accuracy. We review recent advancements in FL, focusing on privacy-preserving techniques such as differential privacy, secure multi-party computation, and homomorphic encryption, and their impact on model performance. Findings: Through a comprehensive analysis of case studies and empirical research, we highlight the potential of FL to revolutionize healthcare applications, including medical imaging, electronic health records (EHR) analysis, and genomic research. We discuss recent advancements, key challenges, and innovative solutions, drawing insights from various studies. Implications to Theory, Practice and Policy: Finally, we highlight future directions and provide practical recommendations for researchers and professionals looking to implement FL in medical settings.