Federated Learning and Explainable AI for Personalized Healthcare in Resource-Limited Settings

Federated Learning and Explainable AI for Personalized Healthcare in Resource-Limited Settings | 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 Research Article Federated Learning and Explainable AI for Personalized Healthcare in Resource-Limited Settings Milad Rahmati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5691431/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 Artificial Intelligence (AI) has transformed healthcare, significantly advancing diagnostic tools, treatment methodologies, and personalized care systems. Despite these advancements, the adoption of AI in resource-constrained environments faces persistent barriers, including data privacy concerns, limited computational resources, and the need for interpretable models. This paper introduces an innovative federated learning framework, integrated with Explainable AI (XAI), to tackle these challenges. The framework enables collaborative training across distributed healthcare institutions while safeguarding patient data privacy and offering clinical decision-making transparency. Additionally, it is optimized for low-resource environments and effectively processes multi-modal healthcare data. Experimental results indicate that the proposed model outperforms conventional AI systems in predictive accuracy, communication efficiency, and interpretability. This work emphasizes the importance of scalable, secure, and interpretable AI solutions in advancing personalized medicine globally, particularly for diverse and underserved populations. Federated Learning Explainable Artificial Intelligence Personalized Healthcare Resource-Limited Settings Multi-Modal Data Data Privacy Decentralized AI Clinical Decision-Making 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-5691431","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405275251,"identity":"26666834-cc33-4a61-a179-818815f11ff2","order_by":0,"name":"Milad Rahmati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYHACNhBhx0+qFoNkyQZStTBuOECset3Zh589ulHzh9n4Ro7hA4YaO8JazM6lmRvnHDPgM7uRY2zAcCyZCC1nGMykc9gMmM1u5G6TYGxgJkYL+zfpnH8GjJtngLXUE6OFx0w6tw3ofQmwlsNEaSmTzu0zTpY48/6zQcKx40Q5bJt0zjc5O/72tMQHH2qqCWtBBQmkahgFo2AUjIJRgB0AAKQfNFiWSW3FAAAAAElFTkSuQmCC","orcid":"","institution":"Western University","correspondingAuthor":true,"prefix":"","firstName":"Milad","middleName":"","lastName":"Rahmati","suffix":""}],"badges":[],"createdAt":"2024-12-21 22:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5691431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5691431/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74524207,"identity":"9b4e10e2-c73d-4537-b05f-4afeba9816b0","added_by":"auto","created_at":"2025-01-23 06:33:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":228719,"visible":true,"origin":"","legend":"","description":"","filename":"1FederatedLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5691431/v1_covered_6d987bd9-212e-43cb-9f69-c6d0a3b98e17.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFederated Learning and Explainable AI for Personalized Healthcare in Resource-Limited Settings\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Federated Learning, Explainable Artificial Intelligence, Personalized Healthcare, Resource-Limited Settings, Multi-Modal Data, Data Privacy, Decentralized AI, Clinical Decision-Making","lastPublishedDoi":"10.21203/rs.3.rs-5691431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5691431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial Intelligence (AI) has transformed healthcare, significantly advancing diagnostic tools, treatment methodologies, and personalized care systems. 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