acePerformance of federated learning models in health services research: A systematic review and meta-analysis
Purpose This study aimed to assess the performance of federated learning (FL) models and compare their performance with local and centralized models. Methods We conducted a systematic search of Ovid MEDLINE and PubMed from inception to June 10, 2025, to identify studies using patient data to train or validate FL algorithms and reporting at least one model performance outcome. Two reviewers independently screened articles and extracted data on study characteristics, FL frameworks and model training methodologies,…
Federated learning models trained on distributed patient data improved predictive performance over single-site local models across AUC, F1, sensitivity, PPV and PRAUC.
Federated learning models showed modest performance losses compared with centralized models trained on pooled patient data across AUC, F1, sensitivity and PRAUC.
Broader application is constrained by lack of standardized reporting and methodological transparency in current studies.
Evidence
- Peer-reviewedAdvances in Medical Sciences2026-08-01
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Truvace Impact Record TRV-2026-0627, v1: “Performance of federated learning models in health services research: A systematic review and meta-analysis.” Truvace, 2026-08-03. /record/TRV-2026-0627 (accessed at citation time). sha256 5e9bfd6d5d96e56f…
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