BlockFed: Blockchain-based Privacy Preserving Federated Learning for 5G-assisted Healthcare Ecosystems

BlockFed: Blockchain-based Privacy Preserving Federated Learning for 5G-assisted Healthcare Ecosystems | 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 BlockFed : Blockchain-based Privacy Preserving Federated Learning for 5G-assisted Healthcare Ecosystems Ashwin Verma, Sunil Pathak, Pronaya Bhattacharya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6848848/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract With the rapid adoption of 5G networks and the growing reliance on digital healthcare, the need for secure, efficient, and privacy-aware data processing has become increasingly critical. This paper presents a novel approach BlockFed , that integrates Federated Learning (FL) with Blockchain (BC) technology to ensure data privacy and model integrity in 5G-assisted healthcare ecosystems. In the proposed system, Patient Health Record (PHR) remains at local Healthcare Entities (HE) such as hospitals and research centers, and only encrypted model updates are shared, effectively preserving user privacy. BC is employed to record and verify model weight transactions, providing tamper-proof integrity and transparency among participating HE. To mitigate the high storage demands of BC, the InterPlanetary File System (IPFS) is utilized for off-chain storage of model weights. Additionally, a lightweight homomorphic encryption scheme is incorporated to protect model parameters during aggregation and transmission. This integrated approach offers a scalable and trustworthy solution for collaborative healthcare intelligence while safeguarding sensitive PHR. Experimental insights and theoretical validation demonstrate the system’s potential for practical deployment in next-generation healthcare infrastructures. Blockchain Federated Learning Privacy-Preservation 5G Healthcare Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Sep, 2025 Reviews received at journal 18 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviews received at journal 28 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 25 Jun, 2025 Editor assigned by journal 13 Jun, 2025 Submission checks completed at journal 13 Jun, 2025 First submitted to journal 08 Jun, 2025 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. 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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-6848848","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477744882,"identity":"a0ae9244-50bc-4ede-baca-96d8bcd65305","order_by":0,"name":"Ashwin Verma","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Ashwin","middleName":"","lastName":"Verma","suffix":""},{"id":477744883,"identity":"0fd7221f-b785-47c9-bf10-d2b14e495d62","order_by":1,"name":"Sunil Pathak","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"","lastName":"Pathak","suffix":""},{"id":477744884,"identity":"444e414b-479b-47bb-830e-d27c1f651890","order_by":2,"name":"Pronaya Bhattacharya","email":"data:image/png;base64,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","orcid":"","institution":"Amity University","correspondingAuthor":true,"prefix":"","firstName":"Pronaya","middleName":"","lastName":"Bhattacharya","suffix":""}],"badges":[],"createdAt":"2025-06-08 17:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6848848/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6848848/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85636463,"identity":"ef5c5a07-c64e-4bea-962c-7049d8b5454b","added_by":"auto","created_at":"2025-06-30 06:05:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1503561,"visible":true,"origin":"","legend":"","description":"","filename":"BlockFEDSPRINGER1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6848848/v1_covered_f69b68e6-73d6-4342-9904-5864718d0046.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cem\u003eBlockFed\u003c/em\u003e: Blockchain-based Privacy Preserving Federated Learning for 5G-assisted Healthcare Ecosystems\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Blockchain, Federated Learning, Privacy-Preservation, 5G, Healthcare","lastPublishedDoi":"10.21203/rs.3.rs-6848848/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6848848/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rapid adoption of 5G networks and the growing reliance on digital healthcare, the need for secure, efficient, and privacy-aware data processing has become increasingly critical. 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