Key result
Systematic review identifies 74 studies evaluating federated learning for ECG-based cardiac monitoring.
Why the study?
This dataset was released so that the Tier-1 evidence base from a systematic review on federated learning for ECG-based cardiac monitoring can be inspected, reused, and extended without reproducing the extraction work.
Systematic Review
Provides a curated dataset of 74 Tier-1 studies on federated learning for ECG-based cardiac monitoring to support reproducible research without repeating extraction work.
Accelerates privacy-preserving ECG monitoring development; extends technical evidence base for clinical translation.
This deposit contains the study-level data supporting the systematic literature review “Federated Learning for ECG-Based Cardiac Monitoring: A Systematic Literature Review” (Bouayad, Ayache, Nasser), conducted following PRISMA 2020. The review covers publications from January 2020 to 7 March 2026, retrieved from IEEE Xplore, PubMed, Scopus and arXiv. Screening was performed independently by two reviewers (Cohen’s κ = 0.82). The file tier1_studies_characteristics.csv lists the 74 Tier-1 studies (8 Tier-1A core studies with quality score ≥ 4, and 66 Tier-1B studies). For each study it reports: identifier, tier, quality score, author and year, title, venue, DOI, datasets, federated-learning paradigm, local model, privacy mechanism, non-IID treatment, and best reported metric or deployment type. The file README.md documents the methodology, the column dictionary and the abbreviations used. This dataset is released so that the Tier-1 evidence base can be inspected, reused and extended without reproducing the extraction work. It is released under CC BY 4.0.
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Ouayad et al. (2026) conducted a systematic review in ECG-based cardiac monitoring. Federated learning for ECG-based cardiac monitoring was evaluated on Study-level characteristics and quality scores of Tier-1 studies. Federated learning for ECG-based cardiac monitoring was evaluated across 74 Tier-1 studies (8 Tier-1A with quality score ≥4 and 66 Tier-1B; reviewer agreement κ=0.82).
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