Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0627
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-03T06:07:09.984351Z
status: published
lens: trace
sector: health
headline: Performance of federated learning models in health services research: A systematic review and meta-analysis
dek: 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,…
gain_title: Federated learning models trained on distributed patient data improved predictive performance over single-site local models across AUC, F1, sensitivity, PPV and PRAUC.
problem_title: Federated learning models showed modest performance losses compared with centralized models trained on pooled patient data across AUC, F1, sensitivity and PRAUC.
trace_subject: performance of federated learning models trained on patient data for health services research
gain_reading: Federated learning models trained on distributed patient data improved predictive performance over single-site local models across AUC, F1, sensitivity, PPV and PRAUC.
gain_evidence: FL showed notable gains over local models, improving the AUC by 8.2%, F1 score by 7.9%, sensitivity by 4.1%, PPV by 26.1%, and PRAUC by 1.6% | FL outperformed local models and demonstrated comparable performance to centralized models while preserving data privacy
problem_reading: Federated learning models showed modest performance losses compared with centralized models trained on pooled patient data across AUC, F1, sensitivity and PRAUC.
problem_evidence: Compared with centralized models, FL showed modest losses of 4.1% in AUC, 9.9% in F1 score, 9.3% in sensitivity, and 2.8% in PRAUC
quick_read: A systematic review and meta-analysis of 13 studies covering 247 sites and 158,435 patient samples evaluated federated learning models, mostly using FedAvg, against local and centralized models on diagnostic performance metrics.

The findings matter because they quantify the privacy-performance trade-off for multi-site clinical AI, showing gains over isolated models but small losses versus pooled data, while highlighting that inconsistent reporting limits confidence in generalizing results to broader health research use.
limitation: Broader application is constrained by lack of standardized reporting and methodological transparency in current studies.
tag: Automated dual reading
key_points: Systematic review included 13 studies involving 247 sites and 158,435 samples, with 8 studies in meta-analysis. | Most FL models (85%) used the Federated Averaging (FedAvg) algorithm for parameter aggregation across sites. | Search covered Ovid MEDLINE and PubMed from inception to June 10, 2025 for studies using patient data to train or validate FL algorithms.
rundown: Reviewers screened Ovid MEDLINE and PubMed to June 10, 2025, extracting study characteristics, FL frameworks, and performance metrics, following PRISMA extension for Diagnostic Test Accuracy Studies.

Within each machine learning task, FL metrics were summarized using medians and interquartile ranges and directly compared to local and centralized models trained on the same patient datasets.
sources:
- peer_reviewed | Advances in Medical Sciences | https://doi.org/10.1016/j.advms.2026.07.003 | 2026-08-01
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

5e9bfd6d5d96e56f5094a2ccb3c3fab4c4ab138f1798433c52b799285ddd8c90

previous

0000000000000000000000000000000000000000000000000000000000000000