Federated Learning Approach in Medicine: Enhancing Privacy and Model Quality: A Narrative Review
Abstract
Federated Learning (FL) is a decentralized machine learning paradigm that enables collaborative model training across multiple devices or institutions without sharing raw data, thereby addressing critical privacy concerns in healthcare. This narrative review explores the potential of FL to transform family medicine by enhancing disease prediction, personalized care, and health equity while addressing technical and ethical challenges. FL's applications in medicine include disease prediction, personalized treatment, remote patient monitoring, and improving health equity in resource-limited settings. Despite its promise, FL faces challenges such as data heterogeneity, computational costs, ethical concerns, and regulatory ambiguity. Future directions include hybrid FL architectures, blockchain integration, edge computing, and global health initiatives. This review concludes that FL holds transformative potential for family medicine, offering privacy-preserving, data-driven solutions to improve patient outcomes and bridge healthcare disparities. However, its success depends on addressing technical, ethical, and regulatory barriers through multidisciplinary collaboration and patient-centric governance frameworks.
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