Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness
Abstract
Background: Data silos and privacy constraints limit the centralized development of machine learning models for cardiovascular disease. Federated learning enables multi-institutional training without sharing raw patient records, but the evidence base in cardiology and deployment-grade evaluation remains uneven.
Objective: To synthesize how federated learning has been implemented for cardiovascular disease prediction and to identify factors that determine clinical translation readiness, including heterogeneity handling, validation quality, privacy and security safeguards, and operational feasibility.
Methods: A systematic literature review was conducted in PubMed, Web of Science, and IEEE Xplore, with supplementary searches in arXiv and Google Scholar, covering studies published from January 2022 through December 2025. Studies applying federated learning to clinically meaningful cardiovascular disease prediction tasks were included. We extracted data on clinical tasks, modalities, federation types, training strategies, evaluation designs, and deployment considerations, and synthesized the findings qualitatively.
Results: Twenty-two studies were included, spanning early screening, clinical diagnosis, prognostic evaluation, and emerging treatment-related decision-support applications. Modalities included electronic health records, electrocardiograms, phonocardiograms, echocardiography, cardiac imaging, and wearable data. Most studies used horizontal federated learning with…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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