{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:07:47Z","timestamp":1782403667254,"version":"3.54.5"},"reference-count":77,"publisher":"Georg Thieme Verlag KG","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Semin Neurol"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Neurological disorders affect hundreds of millions globally, yet translating artificial intelligence (AI) advances into clinical practice remains challenging due to fragmented, privacy-sensitive datasets. Federated learning (FL) has emerged as a promising paradigm, enabling collaborative model training across institutions without sharing raw patient data. This review synthesizes FL applications in neurology from 2020 to 2025, spanning neuroimaging, electrophysiology, and electronic health records. We analyze real-world deployments, highlight algorithmic trends, and discuss technical, regulatory, and organizational barriers to clinical translation. While FL demonstrates feasibility in tasks such as brain tumor segmentation, multiple sclerosis lesion detection, and electronic health record-based predictive modeling, verified clinical implementations remain scarce. We outline strategies to enhance adoption, including privacy-preserving techniques, standardized infrastructures, domain-adaptive algorithms, and cross-disciplinary collaboration. By bridging technical innovation with regulatory compliance and operational scalability, FL holds significant potential to advance precision neurology while safeguarding patient privacy.<\/jats:p>","DOI":"10.1055\/a-2769-6752","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:14:00Z","timestamp":1767053640000},"page":"038-048","source":"Crossref","is-referenced-by-count":2,"title":["Federated Learning in Neurology: Bridging Data Privacy and Artificial Intelligence for Brain Health"],"prefix":"10.1055","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0724-1816","authenticated-orcid":false,"given":"Sahar","family":"Soltanieh","sequence":"additional","affiliation":[{"name":"Division of Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farzad","family":"Khalvati","sequence":"additional","affiliation":[{"name":"Division of Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada"},{"name":"Department of Medical Imaging, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E. Ann","family":"Yeh","sequence":"additional","affiliation":[{"name":"Division of Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada"},{"name":"Department of Paediatrics (Neurology), University of Toronto, Hospital for Sick Children, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"issue":"05","key":"ref1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/S1474-4422(18)30499-X","article-title":"Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016","volume":"18","author":"V L Feigin","year":"2019","journal-title":"Lancet Neurol"},{"issue":"04","key":"ref2","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/S1474-4422(24)00038-3","article-title":"Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021","volume":"23","author":"GBD 2021 Nervous System Disorders Collaborators","year":"2024","journal-title":"Lancet Neurol"},{"issue":"16","key":"ref3","doi-asserted-by":"crossref","first-page":"5913","DOI":"10.3390\/jcm14165913","article-title":"Artificial intelligence in Alzheimer's disease diagnosis and prognosis using PET-MRI: a narrative review of high-impact literature post-Tauvid approval","volume":"14","author":"R C Christodoulou","year":"2025","journal-title":"J Clin Med"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"103673","DOI":"10.1016\/j.msard.2022.103673","article-title":"Artificial intelligence in the diagnosis of multiple sclerosis: a systematic review","volume":"59","author":"F Nabizadeh","year":"2022","journal-title":"Mult Scler Relat Disord"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1101\/2025.09","article-title":"Multimodal machine learning for diagnosis of multiple sclerosis using optical coherence tomography in pediatric cases","author":"C Chen","year":"2025","journal-title":"medRxiv"},{"issue":"10","key":"ref6","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1111\/epi.16333","article-title":"Machine learning applications in epilepsy","volume":"60","author":"B Abbasi","year":"2019","journal-title":"Epilepsia"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"110327","DOI":"10.1016\/j.yebeh.2025.110327","article-title":"Unlocking new frontiers in epilepsy through AI: from seizure prediction to personalized medicine","volume":"166","author":"M A AbuAlrob","year":"2025","journal-title":"Epilepsy Behav"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"1418060","DOI":"10.3389\/fneur.2024.1418060","article-title":"Artificial intelligence in ischemic stroke images: current applications and future directions","volume":"15","author":"Y Liu","year":"2024","journal-title":"Front Neurol"},{"issue":"01","key":"ref9","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/s41467-020-20655-6","article-title":"Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning","volume":"12","author":"A Abrol","year":"2021","journal-title":"Nat Commun"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neubiorev.2017.01.002","article-title":"Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications","volume":"74","author":"S Vieira","year":"2017","journal-title":"Neurosci Biobehav Rev"},{"issue":"05","key":"ref11","doi-asserted-by":"crossref","first-page":"051001","DOI":"10.1088\/1741-2552\/ab260c","article-title":"Deep learning-based electroencephalography analysis: a systematic review","volume":"16","author":"Y Roy","year":"2019","journal-title":"J Neural Eng"},{"issue":"02","key":"ref12","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1109\/JBHI.2023.3331626","article-title":"In-distribution and out-of-distribution self-supervised ECG representation learning for arrhythmia detection","volume":"28","author":"S Soltanieh","year":"2024","journal-title":"IEEE J Biomed Health Inform"},{"issue":"01","key":"ref13","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"A Rajkomar","year":"2018","journal-title":"NPJ Digit Med"},{"key":"ref14","first-page":"1","article-title":"Synthetic data in generalizable, learning-based neuroimaging","volume":"2","author":"K Gopinath","year":"2024","journal-title":"Imaging Neurosci (Camb)"},{"key":"ref15","first-page":"1","article-title":"General data protection regulation","volume":"25","year":"2018","journal-title":"InTouch"},{"key":"ref17","first-page":"191","article-title":"Health Insurance Portability and Accountability Act of 1996","volume":"104","year":"1996","journal-title":"Public Law"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"963601","DOI":"10.3389\/fped.2022.963601","article-title":"A survey of registered pharmacological clinical trials on rare neurological diseases in children in 2010-2020","volume":"10","author":"X Chang","year":"2022","journal-title":"Front Pediatr"},{"issue":"01","key":"ref19","doi-asserted-by":"crossref","first-page":"9416","DOI":"10.1038\/s41467-025-64459-y","article-title":"Addressing data heterogeneity in distributed medical imaging with heterosync learning","volume":"16","author":"H T Hu","year":"2025","journal-title":"Nat Commun"},{"issue":"01","key":"ref20","doi-asserted-by":"crossref","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":"M J Sheller","year":"2020","journal-title":"Sci Rep"},{"issue":"02","key":"ref21","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/JBHI.2021.3096127","article-title":"Personalized real-time federated learning for epileptic seizure detection","volume":"26","author":"S Baghersalimi","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"1167612","DOI":"10.3389\/fnins.2023.1167612","article-title":"Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning","volume":"17","author":"D Liu","year":"2023","journal-title":"Front Neurosci"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"102872","DOI":"10.1016\/j.artmed.2024.102872","article-title":"Improving multiple sclerosis lesion segmentation across clinical sites: a federated learning approach with noise-resilient training","volume":"152","author":"L Bai","year":"2024","journal-title":"Artif Intell Med"},{"key":"ref24","first-page":"1273","volume-title":"Communication-efficient learning of deep networks from decentralized data","author":"B McMahan","year":"2017"},{"issue":"02","key":"ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298981","article-title":"Federated machine learning: concept and applications","volume":"10","author":"Q Yang","year":"2019","journal-title":"ACM Trans Intell Syst Technol"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1911.09824","article-title":"Parallel distributed logistic regression for vertical federated learning without third-party coordinator","author":"S Yang","year":"2019"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"63229","DOI":"10.1109\/ACCESS.2021.3075203","article-title":"Vulnerabilities in federated learning","volume":"9","author":"N Bouacida","year":"2021","journal-title":"IEEE Access"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"e46165","DOI":"10.2196\/46165","article-title":"Privacy-preserving federated model predicting bipolar transition in patients with depression: prediction model development study","volume":"25","author":"D Y Lee","year":"2023","journal-title":"J Med Internet Res"},{"issue":"01","key":"ref29","doi-asserted-by":"crossref","first-page":"7615","DOI":"10.1038\/s41467-024-51172-5","article-title":"An international study presenting a federated learning AI platform for pediatric brain tumors","volume":"15","author":"E H Lee","year":"2024","journal-title":"Nat Commun"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"1620469","DOI":"10.3389\/fneur.2025.1620469","article-title":"Federated learning for lesion segmentation in multiple sclerosis: a real-world multi-center feasibility study","volume":"16","author":"S Hindawi","year":"2025","journal-title":"Front Neurol"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"1207721","DOI":"10.3389\/fninf.2023.1207721","article-title":"Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility","volume":"17","author":"D Martin","year":"2023","journal-title":"Front Neuroinform"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1006\/nimg.2000.0582","article-title":"Voxel-based morphometry\u2013the methods","volume":"11","author":"J Ashburner","year":"2000","journal-title":"Neuroimage"},{"issue":"02","key":"ref33","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1006\/nimg.1998.0395","article-title":"Cortical surface-based analysis. I. Segmentation and surface reconstruction","volume":"9","author":"A M Dale","year":"1999","journal-title":"Neuroimage"},{"issue":"01","key":"ref34","doi-asserted-by":"crossref","first-page":"S163","DOI":"10.1016\/j.neuroimage.2008.10.057","article-title":"A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data","volume":"45","author":"V D Calhoun","year":"2009","journal-title":"Neuroimage"},{"issue":"09","key":"ref35","doi-asserted-by":"crossref","first-page":"2302","DOI":"10.1002\/hbm.22065","article-title":"Functional imaging of the hemodynamic sensory gating response in schizophrenia","volume":"34","author":"A R Mayer","year":"2013","journal-title":"Hum Brain Mapp"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3389\/fpsyt.2011.00075","article-title":"Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and bipolar disorder","volume":"2","author":"V D Calhoun","year":"2012","journal-title":"Front Psychiatry"},{"issue":"10","key":"ref37","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (BraTS)","volume":"34","author":"B H Menze","year":"2015","journal-title":"IEEE Trans Med Imaging"},{"issue":"04","key":"ref38","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods","volume":"27","author":"C R Jack Jr","year":"2008","journal-title":"J Magn Reson Imaging"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"104784","DOI":"10.1016\/j.jbi.2025.104784","article-title":"Federated Bayesian network learning from multi-site data","volume":"163","author":"S Liu","year":"2025","journal-title":"J Biomed Inform"},{"issue":"04","key":"ref40","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TBME.2022.3210940","article-title":"Federated multi-task learning for joint diagnosis of multiple mental disorders on MRI scans","volume":"70","author":"Z-A Huang","year":"2023","journal-title":"IEEE Trans Biomed Eng"},{"issue":"16","key":"ref41","doi-asserted-by":"crossref","first-page":"5095","DOI":"10.3390\/s24165095","article-title":"PSA-FL-CDM: a novel federated learning-based consensus model for post-stroke assessment","volume":"24","author":"N Razfar","year":"2024","journal-title":"Sensors (Basel)"},{"issue":"02","key":"ref43","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1227\/neu.0000000000002198","article-title":"Methods and impact for using federated learning to collaborate on clinical research","volume":"92","author":"A TM Cheung","year":"2023","journal-title":"Neurosurgery"},{"issue":"03","key":"ref44","doi-asserted-by":"crossref","first-page":"100945","DOI":"10.1016\/j.patter.2024.100945","article-title":"Federated learning for multi-omics: a performance evaluation in Parkinson's disease","volume":"5","author":"B P Danek","year":"2024","journal-title":"Patterns (N Y)"},{"issue":"14","key":"ref46","doi-asserted-by":"crossref","first-page":"6578","DOI":"10.3390\/s23146578","article-title":"Patient-specific preictal pattern-aware epileptic seizure prediction with federated learning","volume":"23","author":"R Saemaldahr","year":"2023","journal-title":"Sensors (Basel)"},{"issue":"01","key":"ref48","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1038\/s41746-025-01788-8","article-title":"Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data","volume":"8","author":"A Pirmani","year":"2025","journal-title":"NPJ Digit Med"},{"issue":"01","key":"ref49","doi-asserted-by":"crossref","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":"S Pati","year":"2022","journal-title":"Nat Commun"},{"issue":"03","key":"ref51","doi-asserted-by":"crossref","first-page":"e220082","DOI":"10.1148\/ryai.220082","article-title":"Influence of data distribution on federated learning performance in tumor segmentation","volume":"5","author":"G Luo","year":"2023","journal-title":"Radiol Artif Intell"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2112.10001","article-title":"Cross-domain federated learning in medical imaging","author":"V S Parekh","year":"2021"},{"key":"ref54","first-page":"383","volume-title":"FedCostWAvg: A new averaging for better federated learning","author":"L M\u00e4chler","year":"2021"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2404.10026","article-title":"Distributed federated learning-based deep learning model for privacy MRI brain tumor detection","author":"L Zhou","year":"2024"},{"key":"ref57","doi-asserted-by":"crossref","first-page":"110804","DOI":"10.1016\/j.asoc.2023.110804","article-title":"EDCNNs: federated learning enabled evolutionary deep convolutional neural network for Alzheimer disease detection","volume":"147","author":"A Lakhan","year":"2023","journal-title":"Appl Soft Comput"},{"key":"ref58","doi-asserted-by":"crossref","first-page":"777030","DOI":"10.3389\/fradi.2021.777030","article-title":"Integrating transcriptomics, genomics, and imaging in Alzheimer's disease: a federated model","volume":"1","author":"J Wu","year":"2022","journal-title":"Front Radiol"},{"issue":"12","key":"ref61","doi-asserted-by":"crossref","first-page":"3651","DOI":"10.1109\/TMI.2023.3300725","article-title":"Federated domain adaptation via transformer for multi-site Alzheimer's disease diagnosis","volume":"42","author":"B Lei","year":"2023","journal-title":"IEEE Trans Med Imaging"},{"key":"ref62","doi-asserted-by":"crossref","first-page":"1617883","DOI":"10.3389\/fncom.2025.1617883","article-title":"Privacy-preserving dementia classification from EEG via hybrid-fusion EEGNetv4 and federated learning","volume":"19","author":"M Umair","year":"2025","journal-title":"Front Comput Neurosci"},{"issue":"10","key":"ref63","doi-asserted-by":"crossref","first-page":"e20891","DOI":"10.2196\/20891","article-title":"Federated learning on clinical benchmark data: Performance assessment","volume":"22","author":"G H Lee","year":"2020","journal-title":"J Med Internet Res"},{"issue":"02","key":"ref64","doi-asserted-by":"crossref","first-page":"450","DOI":"10.3390\/s22020450","article-title":"Federated learning in edge computing: A systematic survey","volume":"22","author":"H G Abreha","year":"2022","journal-title":"Sensors (Basel)"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2105.05874","article-title":"The Federated Tumor Segmentation (FeTS) challenge","author":"S Pati","year":"2021"},{"issue":"04","key":"ref69","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.nic.2005.09.008","article-title":"The Alzheimer's disease neuroimaging initiative","volume":"15","author":"S G Mueller","year":"2005","journal-title":"Neuroimaging Clin N Am"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1101\/2019.11.18.19004147","article-title":"OASIS-3: Longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease","author":"P J LaMontagne","year":"2019","journal-title":"medRxiv"},{"issue":"06","key":"ref71","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1038\/mp.2013.78","article-title":"The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism","volume":"19","author":"A Di Martino","year":"2014","journal-title":"Mol Psychiatry"},{"issue":"04","key":"ref72","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s12021-017-9338-9","article-title":"Multimodal neuroimaging in schizophrenia: description and dissemination","volume":"15","author":"C J Aine","year":"2017","journal-title":"Neuroinformatics"},{"key":"ref73","volume-title":"Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment","author":"A H Shoeb","year":"2009"},{"issue":"01","key":"ref74","doi-asserted-by":"crossref","first-page":"190039","DOI":"10.1038\/sdata.2019.39","article-title":"A dataset of neonatal EEG recordings with seizure annotations","volume":"6","author":"N J Stevenson","year":"2019","journal-title":"Sci Data"},{"issue":"01","key":"ref75","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1186\/s12911-020-01332-6","article-title":"Explainability for artificial intelligence in healthcare: a multidisciplinary perspective","volume":"20","author":"J Amann","year":"2020","journal-title":"BMC Med Inform Decis Mak"},{"issue":"01","key":"ref76","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"E J Topol","year":"2019","journal-title":"Nat Med"},{"key":"ref77","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"T Li","year":"2020","journal-title":"Proc Mach Learn Syst."},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2003.00295","article-title":"Adaptive federated optimization","author":"S Reddi","year":"2020"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2102.07623","article-title":"FedBN: federated learning on non-IID features via local batch normalization","author":"X Li","year":"2021"},{"key":"ref81","first-page":"21394","article-title":"Personalized federated learning with Moreau envelopes","volume":"33","author":"C T Dinh","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref82","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy","volume":"9","author":"C Dwork","year":"2014","journal-title":"Found Trends Theor Comput Sci."},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1611.04482","article-title":"Practical secure aggregation for federated learning on user-held data","author":"K Bonawitz","year":"2016"},{"issue":"04","key":"ref84","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3214303","article-title":"A survey on homomorphic encryption schemes: theory and implementation","volume":"51","author":"A Acar","year":"2018","journal-title":"ACM Comput Surv"},{"key":"ref85","volume-title":"An introduction to secret-sharing-based secure multiparty computation. Cryptology ePrint Archive","author":"D Escudero","year":"2022"},{"key":"ref87","doi-asserted-by":"crossref","first-page":"110424","DOI":"10.1016\/j.patcog.2024.110424","article-title":"Federated learning for medical image analysis: a survey","volume":"151","author":"H Guan","year":"2024","journal-title":"Pattern Recognit"},{"issue":"07","key":"ref90","doi-asserted-by":"crossref","first-page":"1996","DOI":"10.1109\/TMI.2022.3220757","article-title":"Federated learning of generative image priors for MRI reconstruction","volume":"42","author":"G Elmas","year":"2023","journal-title":"IEEE Trans Med Imaging"},{"issue":"01","key":"ref93","doi-asserted-by":"crossref","first-page":"23578","DOI":"10.1038\/s41598-025-07807-8","article-title":"A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans","volume":"15","author":"A Al-Saleh","year":"2025","journal-title":"Sci Rep"},{"issue":"06","key":"ref94","doi-asserted-by":"crossref","first-page":"3909","DOI":"10.1007\/s10278-025-01484-9","article-title":"Federated learning framework for brain tumor detection using MRI images in non-IID data distributions","volume":"38","author":"M DZ Muntaqim","year":"2025","journal-title":"J Imaging Inform Med"},{"key":"ref95","doi-asserted-by":"crossref","first-page":"e2459","DOI":"10.7717\/peerj-cs.2459","article-title":"Enhancing Alzheimer's disease classification through split federated learning and GANs for imbalanced datasets","volume":"10","author":"G Narayanee Nimeshika","year":"2024","journal-title":"PeerJ Comput Sci"}],"container-title":["Seminars in Neurology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-2769-6752.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T05:39:13Z","timestamp":1772602753000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-2769-6752"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,29]]},"references-count":77,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2026,3,3]]},"published-print":{"date-parts":[[2026,2]]}},"URL":"https:\/\/doi.org\/10.1055\/a-2769-6752","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["0271-8235","1098-9021"],"issn-type":[{"value":"0271-8235","type":"print"},{"value":"1098-9021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,29]]}}}