SplitFed Learning for Dementia Diagnosis: A Privacy-Preserving Framework for Distributed Healthcare

SplitFed Learning for Dementia Diagnosis: A Privacy-Preserving Framework for Distributed Healthcare | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article SplitFed Learning for Dementia Diagnosis: A Privacy-Preserving Framework for Distributed Healthcare Neha Kalbande, Amar Nath Patra, Raj Mani Shukla, Oliver Faust This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7593311/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dementia affects millions of people worldwide, posing significant challenges for early diagnosis, monitoring, and treatment. With the growing adoption of artificial intelligence (AI) techniques in healthcare, it is crucial to address critical concerns related to data privacy and computational efficiency when developing AI-driven solutions for dementia care. AI-based methods require large amounts of patient data for training, this raises substantial privacy risks due to the sensitive nature of health information. The lack of robust privacy-preserving analysis techniques, combined with the increasing volume of health data, underscores the need for secure and efficient modeling approaches.In this study, we propose the application of SplitFed learning to safeguard patient data while optimizing computational resources in dementia diagnosis and prediction tasks. In proposed scheme, a AI model is divided between client and server for collaborative learning. Experiments were conducted with various client configurations, achieving an accuracy of 95% when the SplitFed model was trained with specific numbers of clients and communication rounds. The results demonstrate that SplitFed learning significantly enhances data privacy without compromising the accuracy of dementia predictions. Furthermore, it substantially reduces the processing burden on individual devices, highlighting its potential for real-time deployment in resource-constrained healthcare environments. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing SplitFed learning Privacy-preserving machine learning Smart healthcare Dementia diagnosis Data protection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7593311","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":524902431,"identity":"4328b01b-f46d-4fbf-9f41-dc30d1c94a36","order_by":0,"name":"Neha Kalbande","email":"","orcid":"","institution":"Anglia Ruskin University","correspondingAuthor":false,"prefix":"","firstName":"Neha","middleName":"","lastName":"Kalbande","suffix":""},{"id":524902432,"identity":"8ac80c8a-6927-4ced-8d6f-13d8fa22a711","order_by":1,"name":"Amar Nath Patra","email":"","orcid":"","institution":"Radford 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