What question did this study set out to answer?

To explore how federated learning can enhance privacy and model quality in medical applications.

March 27, 2026Open Access

Federated Learning Approach in Medicine: Enhancing Privacy and Model Quality: A Narrative Review

Key Points

To explore how federated learning can enhance privacy and model quality in medical applications.
Narrative review of existing literature on federated learning in healthcare.
Analysis of technical, ethical, and regulatory challenges.
Evaluation of FL applications, including personalized care and health equity.
Federated learning improves privacy while enabling collaborative model training across healthcare settings.
Addresses challenges like data heterogeneity and computational costs.
Shows potential applications in disease prediction and personalized treatment.

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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