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

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dc.contributor.authorColak, Cemil
dc.contributor.authorTetik, Burcu Kayhan
dc.date.accessioned2026-04-04T13:32:57Z
dc.date.available2026-04-04T13:32:57Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractFederated 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.
dc.identifier.doi10.18521/ktd.1664270
dc.identifier.endpage335
dc.identifier.issn1309-3878
dc.identifier.issue3
dc.identifier.orcid0000-0001-5406-098X
dc.identifier.startpage331
dc.identifier.urihttps://doi.org/10.18521/ktd.1664270
dc.identifier.urihttps://hdl.handle.net/11616/108820
dc.identifier.volume17
dc.identifier.wosWOS:001614872200014
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherDuzce Univ, Fac Medicine
dc.relation.ispartofKonuralp Tip Dergisi
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectFederated Learning
dc.subjectPrivacy-Preserving
dc.subjectMachine Learning
dc.subjectHealthcare Informatics
dc.subjectPersonalized Medicine
dc.subjectHealth Equity
dc.titleFederated Learning Approach in Medicine: Enhancing Privacy and Model Quality: A Narrative Review
dc.typeReview

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