{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T05:49:22Z","timestamp":1784958562534,"version":"3.55.0"},"reference-count":119,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. This paper provides a gentle introduction to this approach in bioinformatics, and is the first to review key applications in proteomics, genome-wide association studies (GWAS), single-cell and multi-omics studies in their legal as well as methodological and infrastructural challenges. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads to a faster and more robust exploration and translation of results. More widespread use of federated learning may have a similar impact in bioinformatics, allowing academic and clinical institutions to access many combinations of genotypic, phenotypic and environmental information that are undercovered or not included in existing biobanks.<\/jats:p>","DOI":"10.3389\/fdgth.2025.1644291","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T06:22:55Z","timestamp":1763446975000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities"],"prefix":"10.3389","volume":"7","author":[{"given":"Daniele","family":"Malpetti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Scutari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Gualdi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"van Setten","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sander","family":"van der Laan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saskia","family":"Haitjema","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron Mark","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabelle","family":"Hering","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Mangili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"B1","first-page":"1","article-title":"General data protection regulation (GDPR)","volume":"679","year":"2016","journal-title":"Off J Eur Union"},{"key":"B2","first-page":"1","article-title":"AI act","volume":"1689","year":"2024","journal-title":"Off J Eur Union"},{"key":"B3","article-title":"Health Insurance Portability and Accountability Act of 1996 (1996). Pub. L. No. 104-191, 110 Stat. 1936","year":""},{"key":"B4","article-title":"National Artificial Intelligence Initiative Act of 2020 (2020). Public Law No: 116-283, Division E","year":""},{"key":"B5","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/s11948-017-9901-7","article-title":"Artificial intelligence and the \u2018good society\u2019: the US, EU, and UK approach","volume":"24","author":"Cath","year":"2018","journal-title":"Sci Eng Ethics"},{"key":"B6","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":""},{"key":"B7","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-96896-0","volume-title":"Federated Learning: A Comprehensive Overview of Methods and Applications","author":"Ludwig","year":"2022"},{"key":"B8","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/s23010031","article-title":"Federated learning attacks revisited: a critical discussion of gaps, assumptions, and evaluation setups","volume":"23","author":"Wainakh","year":"2022","journal-title":"Sensors"},{"key":"B9","doi-asserted-by":"publisher","first-page":"102402","DOI":"10.1016\/j.cose.2021.102402","article-title":"Privacy preservation in federated learning: an insightful survey from the GDPR perspective","volume":"110","author":"Truong","year":"2021","journal-title":"Comput Secur"},{"key":"B10","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-60548-3_18","article-title":"Federated learning for breast density classification: a real-world implementation","author":"Roth","year":""},{"key":"B11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","article-title":"Federated learning for predicting clinical outcomes in patients with COVID-19","volume":"27","author":"Dayan","year":"2021","journal-title":"Nat Med"},{"key":"B12","doi-asserted-by":"publisher","first-page":"7346","DOI":"10.1038\/s41467-022-33407-5","article-title":"Federated learning enables big data for rare cancer boundary detection","volume":"13","author":"Pati","year":"2022","journal-title":"Nat Commun"},{"key":"B13","doi-asserted-by":"publisher","first-page":"2331","DOI":"10.1021\/acs.jcim.3c00799","article-title":"MELLODDY: cross-pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information","volume":"64","author":"Heyndrickx","year":"2023","journal-title":"J Chem Inf Model"},{"key":"B14","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.ijmedinf.2018.01.007","article-title":"Federated learning of predictive models from federated electronic health records","volume":"112","author":"Brisimi","year":"2018","journal-title":"Int J Med Inform"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41666-020-00082-4","article-title":"Federated learning for healthcare informatics","volume":"5","author":"Xu","year":"2020","journal-title":"J Healthc Inform Res"},{"key":"B16","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-08999-2_1","article-title":"A review of medical federated learning: applications in oncology and cancer research","author":"Chowdhury","year":""},{"key":"B17","volume-title":"Parallel Computing for Bioinformatics and Computational Biology: Models, Enabling Technologies, and Case Studies","author":"Zomaya","year":"2006"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1694","DOI":"10.1093\/bib\/bbaa019","article-title":"A survey of gene expression meta-analysis: methods and applications","volume":"22","author":"Toro-Dom\u00ednguez","year":"2021","journal-title":"Brief Bioinformatics"},{"key":"B19","doi-asserted-by":"publisher","first-page":"W535","DOI":"10.1093\/nar\/gkad464","article-title":"Sfkit: a web-based toolkit for secure and federated genomic analysis","volume":"51","author":"Mendelsohn","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-021-02553-2","article-title":"Flimma: a federated and privacy-aware tool for differential gene expression analysis","volume":"22","author":"Zolotareva","year":"2021","journal-title":"Genome Biol"},{"key":"B21","doi-asserted-by":"publisher","first-page":"e33720","DOI":"10.2196\/33720","article-title":"Next-generation capabilities in trusted research environments: interview study","volume":"24","author":"Kavianpour","year":"2022","journal-title":"J Med Internet Res"},{"key":"B22","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age","volume":"12","author":"Sudlow","year":"2015","journal-title":"PLoS Med"},{"key":"B23","article-title":"Federated European Genome-Phenome Archive (FEGA) (2024)","year":""},{"key":"B24","doi-asserted-by":"crossref","first-page":"2983","DOI":"10.1109\/COMST.2023.3315746","article-title":"Decentralized federated learning: fundamentals, state of the art, frameworks, trends, and challenges","volume":"5","author":"Beltr\u00e1n","year":"2023","journal-title":"IEEE Commun Surv Tutor"},{"key":"B25","article-title":"Hyfed: a hybrid federated framework for privacy-preserving machine learning","author":"Nasirigerdeh","year":""},{"key":"B26","doi-asserted-by":"crossref","DOI":"10.1201\/9780429292835","volume-title":"The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions","author":"Scutari","year":"2023"},{"key":"B27","doi-asserted-by":"publisher","first-page":"8693","DOI":"10.1109\/ACCESS.2022.3141913","article-title":"Decentralized federated learning for healthcare networks: a case study on tumor segmentation","volume":"10","author":"Camajori Tedeschini","year":"2022","journal-title":"IEEE Access"},{"key":"B28","doi-asserted-by":"publisher","first-page":"5257","DOI":"10.1007\/s13042-024-02234-z","article-title":"Comparative analysis of open-source federated learning frameworks\u2014a literature-based survey and review","volume":"15","author":"Riedel","year":"2024","journal-title":"Int J Mach Learn Cybern"},{"key":"B29","article-title":"TensorFlow federated: machine learning on decentralized data (2024)","year":""},{"key":"B30","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-70604-3_5","article-title":"Pysyft: a library for easy federated learning","author":"Ziller","year":""},{"key":"B31","article-title":"Flower: a friendly federated learning research framework","author":"Beutel","year":""},{"key":"B32","doi-asserted-by":"publisher","first-page":"214001","DOI":"10.1088\/1361-6560\/ac97d9","article-title":"OpenFL: the open federated learning library","volume":"67","author":"Foley","year":"2022","journal-title":"Phys Med Biol"},{"key":"B33","article-title":"Cross-silo federated learning: challenges and opportunities","author":"Huang","year":""},{"key":"B34","doi-asserted-by":"publisher","first-page":"12598","DOI":"10.1038\/s41598-020-69250-1","article-title":"Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data","volume":"10","author":"Sheller","year":"2020","journal-title":"Sci Rep"},{"key":"B35","doi-asserted-by":"publisher","first-page":"9587","DOI":"10.1109\/TNNLS.2022.3160699","article-title":"Towards personalized federated learning","volume":"34","author":"Tan","year":"2022","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"B36","doi-asserted-by":"publisher","first-page":"3710","DOI":"10.1109\/TNNLS.2020.3015958","article-title":"Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints","volume":"32","author":"Sattler","year":"2021","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"B37","first-page":"16937","article-title":"Inverting gradients-how easy is it to break privacy in federated learning?","volume":"33","author":"Geiping","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"B38","first-page":"22911","article-title":"Reconstructing training data from trained neural networks","volume":"35","author":"Haim","year":"2022","journal-title":"Adv Neural Inf Process Syst"},{"key":"B39","doi-asserted-by":"crossref","DOI":"10.1109\/SP.2017.41","article-title":"Membership inference attacks against machine learning models","author":"Shokri","year":""},{"key":"B40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3523273","article-title":"Membership inference attacks on machine learning: a survey","volume":"54","author":"Hu","year":"2022","journal-title":"ACM Comput Surv"},{"key":"B41","doi-asserted-by":"publisher","first-page":"e1000167","DOI":"10.1371\/journal.pgen.1000167","article-title":"Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays","volume":"4","author":"Homer","year":"2008","journal-title":"PLoS Genet"},{"key":"B42","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1093\/bioinformatics\/btv018","article-title":"Deterministic identification of specific individuals from GWAS results","volume":"31","author":"Cai","year":"2015","journal-title":"Bioinformatics"},{"key":"B43","doi-asserted-by":"publisher","first-page":"1266031","DOI":"10.3389\/fdata.2024.1266031","article-title":"Efficacy of federated learning on genomic data: a study on the UK biobank and the 1000 genomes project","volume":"7","author":"Kolobkov","year":"2024","journal-title":"Front Big Data"},{"key":"B44","doi-asserted-by":"crossref","DOI":"10.1145\/2810103.2813677","article-title":"Model inversion attacks that exploit confidence information and basic countermeasures","author":"Fredrikson","year":""},{"key":"B45","doi-asserted-by":"publisher","first-page":"11365","DOI":"10.1109\/JIOT.2021.3128646","article-title":"Data poisoning attacks on federated machine learning","volume":"9","author":"Sun","year":"2022","journal-title":"IEEE Internet Things J"},{"key":"B46","doi-asserted-by":"crossref","DOI":"10.1109\/SP.2018.00057","article-title":"Manipulating machine learning: poisoning attacks and countermeasures for regression learning","author":"Jagielski","year":""},{"key":"B47","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.future.2023.05.002","article-title":"Defending Byzantine attacks in ensemble federated learning: a reputation-based phishing approach","volume":"147","author":"Li","year":"2023","journal-title":"Future Gener Comput Syst"},{"key":"B48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3460427","article-title":"A comprehensive survey of privacy-preserving federated learning: a taxonomy, review, and future directions","volume":"54","author":"Yin","year":"2021","journal-title":"ACM Comput Surv"},{"key":"B49","doi-asserted-by":"crossref","DOI":"10.1145\/1536414.1536440","article-title":"Fully homomorphic encryption using ideal lattices","author":"Gentry","year":""},{"key":"B50","doi-asserted-by":"crossref","DOI":"10.1007\/3-540-48910-X_16","article-title":"Public-key cryptosystems based on composite degree residuosity classes","author":"Paillier","year":""},{"key":"B51","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.ins.2018.10.024","article-title":"Secure multi-party computation: theory, practice and applications","volume":"476","author":"Zhao","year":"2019","journal-title":"Inf Sci (Ny)"},{"key":"B52","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1093\/jamia\/ocab135","article-title":"Differential privacy in health research: a scoping review","volume":"28","author":"Ficek","year":"2021","journal-title":"J Am Med Inform Assoc"},{"key":"B53","article-title":"Locally private Bayesian inference for count models","author":"Schein","year":""},{"key":"B54","doi-asserted-by":"publisher","first-page":"2190","DOI":"10.14778\/3476249.3476272","article-title":"Data synthesis via differentially private Markov random fields","volume":"14","author":"Cai","year":"2021","journal-title":"Proc VLDB Endow"},{"key":"B55","doi-asserted-by":"crossref","DOI":"10.1145\/2976749.2978318","article-title":"Deep learning with differential privacy","author":"Abadi","year":""},{"key":"B56","article-title":"Evaluating differentially private machine learning in practice","author":"Jayaraman","year":""},{"key":"B57","doi-asserted-by":"crossref","DOI":"10.1145\/1250790.1250803","article-title":"Smooth sensitivity and sampling in private data analysis","author":"Nissim","year":""},{"key":"B58","article-title":"Privacy-preserving prediction","author":"Dwork","year":""},{"key":"B59","first-page":"15479","article-title":"Differential privacy has disparate impact on model accuracy","volume":"32","author":"Bagdasaryan","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"B60","doi-asserted-by":"publisher","first-page":"103663","DOI":"10.1016\/j.scs.2021.103663","article-title":"Federated learning enabled digital twins for smart cities: concepts, recent advances, and future directions","volume":"79","author":"Ramu","year":"2022","journal-title":"Sustain Cities Soc"},{"key":"B61","doi-asserted-by":"publisher","first-page":"3688","DOI":"10.1109\/JSAC.2021.3118352","article-title":"Optimizing federated learning in distributed industrial IoT: a multi-agent approach","volume":"39","author":"Zhang","year":"2021","journal-title":"IEEE J Sel Areas Commun"},{"key":"B62","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1007\/978-3-030-63076-8_17","volume-title":"Federated Learning for Open Banking","author":"Long","year":"2020"},{"key":"B63","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s13756-024-01464-8","article-title":"Federated systems for automated infection surveillance: a perspective","volume":"13","author":"van Rooden","year":"2024","journal-title":"Antimicrob Resist Infect Control"},{"key":"B64","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.critrevonc.2015.07.006","article-title":"Proteomics in cancer research: are we ready for clinical practice?","volume":"96","author":"Maes","year":"2015","journal-title":"Crit Rev Oncol Hematol"},{"key":"B65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12920-017-0293-y","article-title":"Differential gene expression in disease: a comparison between high-throughput studies and the literature","volume":"10","author":"Rodriguez-Esteban","year":"2017","journal-title":"BMC Med Genom"},{"key":"B66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-00323-1","article-title":"The future of digital health with federated learning","volume":"3","author":"Rieke","year":"2020","journal-title":"npj Digit Med"},{"key":"B67","doi-asserted-by":"publisher","first-page":"103798","DOI":"10.1016\/j.isci.2022.103798","article-title":"Machine learning for multi-omics data integration in cancer","volume":"25","author":"Cai","year":"2022","journal-title":"Iscience"},{"key":"B68","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1074\/mcp.TIR119.001646","article-title":"DEqMS: a method for accurate variance estimation in differential protein expression analysis","volume":"19","author":"Zhu","year":"2020","journal-title":"Mol Cell Proteom"},{"key":"B69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2014-15-2-r29","article-title":"Voom: precision weights unlock linear model analysis tools for RNA-seq read counts","volume":"15","author":"Law","year":"2014","journal-title":"Genome Biol"},{"key":"B70","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-63772-8_26","article-title":"Federated learning on transcriptomic data: model quality and performance trade-offs","author":"Hannemann","year":""},{"key":"B71","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1186\/s12911-022-02014-1","article-title":"Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources","volume":"22","author":"Li","year":"2022","journal-title":"BMC Med Inform Decis Mak"},{"key":"B72","doi-asserted-by":"publisher","first-page":"bbac473","DOI":"10.1093\/bib\/bbac473","article-title":"Privacy-aware estimation of relatedness in admixed populations","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"},{"key":"B73","doi-asserted-by":"publisher","first-page":"e1012142","DOI":"10.1371\/journal.pcbi.1012142","article-title":"FedGMMAT: federated generalized linear mixed model association tests","volume":"20","author":"Li","year":"2024","journal-title":"PLoS Comput Biol"},{"key":"B74","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1038\/s41588-025-02109-1","article-title":"Secure and federated genome-wide association studies for biobank-scale datasets","volume":"57","author":"Cho","year":"2025","journal-title":"Nat Genet"},{"key":"B75","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1038\/s41588-021-00870-7","article-title":"Computationally efficient whole-genome regression for quantitative and binary traits","volume":"53","author":"Mbatchou","year":"2021","journal-title":"Nat Genet"},{"key":"B76","doi-asserted-by":"publisher","first-page":"bbad507","DOI":"10.1093\/bib\/bbad507","article-title":"Scfed: federated learning for cell type classification with scRNA-seq","volume":"25","author":"Wang","year":"2024","journal-title":"Brief Bioinform"},{"key":"B77","first-page":"89","article-title":"FedTree: a federated learning system for trees","volume":"5","author":"Li","year":"2023","journal-title":"Proc Mach Learn Syst"},{"key":"B78","doi-asserted-by":"publisher","first-page":"bbad269","DOI":"10.1093\/bib\/bbad269","article-title":"AFEI: adaptive optimized vertical federated learning for heterogeneous multi-omics data integration","volume":"24","author":"Wang","year":"2023","journal-title":"Brief Bioinform"},{"key":"B79","doi-asserted-by":"publisher","first-page":"100945","DOI":"10.1016\/j.patter.2024.100945","article-title":"Federated learning for multi-omics: a performance evaluation in Parkinson\u2019s disease","volume":"5","author":"Danek","year":"2024","journal-title":"Patterns"},{"key":"B80","doi-asserted-by":"publisher","first-page":"1","DOI":"10.59275\/j.melba.2022-8g82","article-title":"Semi-supervised federated peer learning for skin lesion classification","volume":"1","author":"Bdair","year":"2022","journal-title":"Mach Learn Biomed Imaging"},{"key":"B81","doi-asserted-by":"publisher","first-page":"1932","DOI":"10.1109\/TMI.2022.3233574","article-title":"Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging","volume":"42","author":"Yan","year":"2023","journal-title":"IEEE Trans Med Imaging"},{"key":"B82","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1609\/aaai.v36i1.19993","article-title":"Harmofl: harmonizing local and global drifts in federated learning on heterogeneous medical images","volume":"36","author":"Jiang","year":"2022","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"B83","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1001\/jamadermatol.2023.5550","article-title":"Federated learning for decentralized artificial intelligence in melanoma diagnostics","volume":"160","author":"Haggenm\u00fcller","year":"2024","journal-title":"JAMA Dermatol"},{"key":"B84","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.1038\/s41598-022-07186-4","article-title":"Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease","volume":"12","author":"Linardos","year":"2022","journal-title":"Sci Rep"},{"key":"B85","doi-asserted-by":"publisher","first-page":"101992","DOI":"10.1016\/j.media.2021.101992","article-title":"Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan","volume":"70","author":"Yang","year":"2021","journal-title":"Med Image Anal"},{"key":"B86","doi-asserted-by":"publisher","first-page":"e42621","DOI":"10.2196\/42621","article-title":"The featurecloud platform for federated learning in biomedicine: unified approach","volume":"25","author":"Matschinske","year":"2023","journal-title":"J Med Internet Res"},{"key":"B87","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1186\/s13059-019-1741-0","article-title":"Emerging technologies towards enhancing privacy in genomic data sharing","volume":"20","author":"Berger","year":"2019","journal-title":"Genome Biol"},{"key":"B88","doi-asserted-by":"publisher","first-page":"5910","DOI":"10.1038\/s41467-021-25972-y","article-title":"Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption","volume":"12","author":"Froelicher","year":"2021","journal-title":"Nat Commun"},{"key":"B89","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s12276-018-0071-8","article-title":"Single-cell RNA sequencing technologies and bioinformatics pipelines","volume":"50","author":"Hwang","year":"2018","journal-title":"Exp Mol Med"},{"key":"B90","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1038\/nri.2017.76","article-title":"Single-cell RNA sequencing to explore immune cell heterogeneity","volume":"18","author":"Papalexi","year":"2018","journal-title":"Nat Rev Immunol"},{"key":"B91","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1093\/bioinformatics\/btz592","article-title":"ACTINN: automated identification of cell types in single cell RNA sequencing","volume":"36","author":"Ma","year":"2020","journal-title":"Bioinformatics"},{"key":"B92","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1038\/s41586-023-06139-9","article-title":"Transfer learning enables predictions in network biology","volume":"618","author":"Theodoris","year":"2023","journal-title":"Nature"},{"key":"B93","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1186\/s13059-025-03684-6","article-title":"FedscGEN: privacy-preserving federated batch effect correction of single-cell RNA sequencing Data","volume":"26","author":"Bakhtiari","year":"2025","journal-title":"Genome Biol"},{"key":"B94","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1038\/s41592-019-0494-8","article-title":"scGEN predicts single-cell perturbation responses","volume":"16","author":"Lotfollahi","year":"2019","journal-title":"Nat Methods"},{"key":"B95","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/nrg3575","article-title":"Systems genetics approaches to understand complex traits","volume":"15","author":"Civelek","year":"2014","journal-title":"Nat Rev Genet"},{"key":"B96","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.1109\/TKDE.2024.3352628","article-title":"Vertical federated learning: concepts, advances, and challenges","volume":"36","author":"Liu","year":"2024","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"B97","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s12859-023-05156-9","article-title":"Evaluation of a decided sample size in machine learning applications","volume":"24","author":"Rajput","year":"2023","journal-title":"BMC Bioinform"},{"key":"B98","doi-asserted-by":"crossref","DOI":"10.1017\/9781316671849","volume-title":"Fundamentals of Medical Imaging","author":"Suetens","year":"2017"},{"key":"B99","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1038\/s41576-022-00455-y","article-title":"Sociotechnical safeguards for genomic data privacy","volume":"23","author":"Wan","year":"2022","journal-title":"Nat Rev Genet"},{"key":"B100","article-title":"Lattigo: a multiparty homomorphic encryption library in go","author":"Mouchet","year":""},{"key":"B101","doi-asserted-by":"crossref","DOI":"10.1145\/3372297.3417885","article-title":"Secure single-server aggregation with (Poly) logarithmic overhead","author":"Bell","year":""},{"key":"B102","doi-asserted-by":"crossref","DOI":"10.1145\/3488659.3493776","article-title":"Secure aggregation for federated learning in flower","author":"Li","year":""},{"key":"B103","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1038\/s41746-024-01293-4","article-title":"Privacy-friendly evaluation of patient data with secure multiparty computation in a European pilot study","volume":"7","author":"Ballhausen","year":"2024","journal-title":"npj Digit Med"},{"key":"B104","doi-asserted-by":"publisher","first-page":"2300695","DOI":"10.2807\/1560-7917.ES.2024.29.38.2300695","article-title":"The potential of federated learning for public health purposes: a qualitative analysis of GDPR compliance, Europe, 2021","volume":"29","author":"Lieftink","year":"2024","journal-title":"Eurosurveillance"},{"key":"B105","doi-asserted-by":"publisher","first-page":"104194","DOI":"10.1016\/j.jbi.2022.104194","article-title":"Studying the association of diabetes and healthcare cost on distributed data from the Maastricht Study and Statistics Netherlands using a privacy-preserving federated learning infrastructure","volume":"134","author":"Sun","year":"2022","journal-title":"J Biomed Inform"},{"key":"B106","first-page":"291","article-title":"A deep dive into technical encryption concepts to better understand cybersecurity & data privacy legal & policy issues","volume":"28","author":"Volini","year":"2020","journal-title":"J Intell Prop Law"},{"key":"B107","doi-asserted-by":"crossref","DOI":"10.1109\/EuroSPW.2018.00021","article-title":"Methods and tools for GDPR compliance through privacy and data protection engineering","author":"Martin","year":""},{"key":"B108","doi-asserted-by":"publisher","first-page":"e0312697","DOI":"10.1371\/journal.pone.0312697","article-title":"Capability and accuracy of usual statistical analyses in a real-world setting using a federated approach","volume":"19","author":"J\u00e9gou","year":"2024","journal-title":"PLoS ONE"},{"key":"B109","first-page":"311","volume-title":"Big Data and Intellectual Property Rights in the Health and Life Sciences","author":"Minssen","year":"2018"},{"key":"B110","doi-asserted-by":"crossref","DOI":"10.1109\/SRDS53918.2021.00038","article-title":"WAFFLE: watermarking in federated learning","author":"Tekgul","year":""},{"key":"B111","article-title":"Data Privacy Framework (2025)","year":""},{"key":"B112","doi-asserted-by":"crossref","DOI":"10.1109\/TPS-ISA62245.2024.00026","article-title":"Federated learning in practice: reflections and projections","author":"Daly","year":""},{"key":"B113","doi-asserted-by":"crossref","DOI":"10.1109\/Trustcom.2015.357","article-title":"Trusted execution environment: what it is, and what it is not","author":"Sabt","year":""},{"key":"B114","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s13042-022-01647-y","article-title":"A survey on federated learning: challenges and applications","volume":"14","author":"Wen","year":"2023","journal-title":"Int J Mach Learn Cybern"},{"key":"B115","doi-asserted-by":"publisher","first-page":"e38137","DOI":"10.1016\/j.heliyon.2024.e38137","article-title":"Federated learning: overview, strategies, applications, tools and future directions","volume":"10","author":"Yurdem","year":"2024","journal-title":"Heliyon"},{"key":"B116","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/COMST.2025.3552524","article-title":"Advances and open challenges in federated foundation models","author":"Ren","year":"2025","journal-title":"IEEE Commun Surv Tutor"},{"key":"B117","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1038\/s41746-025-01482-9","article-title":"Could transparent model cards with layered accessible information drive trust and safety in health AI?","volume":"8","author":"Gilbert","year":"2025","journal-title":"npj Digit Med"},{"key":"B118","article-title":"Applied Model Card (2024)","year":""},{"key":"B119","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"bmj"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1644291\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T06:22:59Z","timestamp":1763446979000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1644291\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"references-count":119,"alternative-id":["10.3389\/fdgth.2025.1644291"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2025.1644291","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,18]]},"article-number":"1644291"}}