Decentralized and Personalized Federated Learning Framework for Privacy Preservation Using IPFS Model Storage Layer in Healthcare

Decentralized and Personalized Federated Learning Framework for Privacy Preservation Using IPFS Model Storage Layer in 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 Research Article Decentralized and Personalized Federated Learning Framework for Privacy Preservation Using IPFS Model Storage Layer in Healthcare Subhajit Ghosh, Avik Kumar Das, Apurba Nandi, Arijeet Ghosh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6160297/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 In the quickly changing healthcare environment, this paper presents a novel framework comprising Decentralized and Personalized Federated Learning (DCPFL), which leverages decentralized model storage using Inter-Planetary File System (IPFS). By addressing data privacy and model personalization, we established a consortium blockchain for model integrity and used IPFS for efficient parameter storage. Our innovative synchronization mechanism enhances collaboration between institutions using gossip protocol while safeguarding patient data by training locally using RMSProp, laying the foundation for safer, secure, efficient and personalized healthcare solutions. Federated learning privacy preservation Internet of medical things machine learning 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. 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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-6160297","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425015860,"identity":"40eca1e4-3e58-48ff-8e36-02923d658035","order_by":0,"name":"Subhajit Ghosh","email":"","orcid":"","institution":"University of Engineering \u0026 Management","correspondingAuthor":false,"prefix":"","firstName":"Subhajit","middleName":"","lastName":"Ghosh","suffix":""},{"id":425015861,"identity":"eabf7800-fb4a-4e4a-a901-e7f5a9c048e2","order_by":1,"name":"Avik Kumar Das","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYFACxmbGBgjLgIGhAsKSACPitJwhSgsDM0ILYxtcC27APyO52XBm2zZ58wbmjR9/zjssp9vAfPA2D4NFHi4tEjcSmxM3tt02nHOArViad9thY7MDbMnWPAwSxTitOXOw+eDDttuMMxh4DKQZtx1O3HaAx0waqCWxAYcOeagWe6AW458/5xyu33aA/xteLQbHG8EOSwRqMZPgbTicYHaAhw2vFkOgFsMZ524nz2BmK7PmOZZuuO0wm7HlHAPcWuQOsz+W7Cm7bTuDvXnzzR811vJmx5sf3nhTUYdTCwIwozAMCKofBaNgFIyCUYAHAADdk1cjYDAm0gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Engineering \u0026 Management","correspondingAuthor":true,"prefix":"","firstName":"Avik","middleName":"Kumar","lastName":"Das","suffix":""},{"id":425015862,"identity":"575b99ab-035b-4921-a664-dc92ace06b91","order_by":2,"name":"Apurba Nandi","email":"","orcid":"","institution":"University of Engineering \u0026 Management","correspondingAuthor":false,"prefix":"","firstName":"Apurba","middleName":"","lastName":"Nandi","suffix":""},{"id":425015863,"identity":"985fcf25-4683-4d26-a358-e1d718a94734","order_by":3,"name":"Arijeet Ghosh","email":"","orcid":"","institution":"University of Engineering \u0026 Management","correspondingAuthor":false,"prefix":"","firstName":"Arijeet","middleName":"","lastName":"Ghosh","suffix":""}],"badges":[],"createdAt":"2025-03-05 08:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6160297/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6160297/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79019233,"identity":"6e2cbce4-d121-49b5-9208-4c129430e783","added_by":"auto","created_at":"2025-03-22 15:23:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":847949,"visible":true,"origin":"","legend":"","description":"","filename":"APersonalizedFederatedLearningFrameworkforPrivacyPreservationUsingDecentralizedModelStorageinHealthcare.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6160297/v1_covered_667d1ec0-2e65-43ba-8691-3216f3a44c49.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decentralized and Personalized Federated Learning Framework for Privacy Preservation Using IPFS Model Storage Layer in Healthcare","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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, privacy preservation, Internet of medical things, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-6160297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6160297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the quickly changing healthcare environment, this paper presents a novel framework comprising Decentralized and Personalized Federated Learning (DCPFL), which leverages decentralized model storage using Inter-Planetary File System (IPFS). 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