ArticleFrontiers in big data2024
Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project.
Article in Frontiers in big data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- Review
- Transcending genome-wide association studies to create useful multi-omic views of glaucoma.Progress in retinal and eye research · 2026Review
- Privacy-preserving federated prediction of health outcomes using multi-center survey data.BMC medical research methodology · 2026Article
- Predicting Physical Appearance from Low Template: State of the Art and Future Perspectives.Genes · 2026Review
- Fedflow: cloud orchestration for federated learning with the FeatureCloud platform.Bioinformatics advances · 2026Article
- Privacy-by-Design with Federated Learning will drive future Rare Disease Research.Journal of neuromuscular diseases · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Data visiting governance: a conceptual framework.Human genomics · 2025Review
- Federated learning for the pathogenicity annotation of genetic variants in multi-site clinical settings.Bioinformatics (Oxford, England) · 2025Article
- Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.Briefings in bioinformatics · 2025Review
- The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.Biomedicines · 2025Review
- Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities.Frontiers in digital health · 2025Review
- Review
- Prequalification of genome-based newborn screening for severe childhood genetic diseases through federated training based on purifying hyperselection.American journal of human genetics · 2024Article
- Future-proofing genomic data and consent management: a comprehensive review of technology innovations.GigaScience · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combining training data from multiple sources increases sample size and reduces confounding, leading to more accurate and less biased machine learning models. In healthcare, however, direct pooling of data is often not allowed by data custodians who are accountable for minimizing the exposure of sensitive information. Federated learning offers a promising solution to this problem by training a model in a decentralized manner thus reducing the risks of data leakage. Although there is increasing utilization of federated learning on clinical data, its efficacy on individual-level genomic data has not been studied. This study lays the groundwork for the adoption of federated learning for genomic data by investigating its applicability in two scenarios: phenotype prediction on the UK Biobank data and ancestry prediction on the 1000 Genomes Project data. We show that federated models trained on data split into independent nodes achieve performance close to centralized models, even in the presence of significant inter-node heterogeneity. Additionally, we investigate how federated model accuracy is affected by communication frequency and suggest approaches to reduce computational complexity or communication costs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.