Blockchain-Enabled Federated Learning with Edge Analytics for Secure and Efficient Electronic Health Records Management

Blockchain-Enabled Federated Learning with Edge Analytics for Secure and Efficient Electronic Health Records Management | 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 Blockchain-Enabled Federated Learning with Edge Analytics for Secure and Efficient Electronic Health Records Management Munusamy S, Jothi K R This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6678464/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 18 You are reading this latest preprint version Abstract The rapid adoption of Federated Learning (FL) in privacy-sensitive domains such as healthcare, IoT, and smart cities highlights its potential to enable collaborative machine learning without compromising data ownership. However, conventional FL frameworks face several critical challenges: high computational overhead at edge devices, significant communication latency due to frequent model updates, vulnerability to model and data poisoning attacks, and limited privacy preservation mechanisms that expose systems to inference risks. These issues hinder the scalability, efficiency, and trustworthiness of FL in real-world, large-scale deployments—particularly in domains like Electronic Health Records (EHR) management, where data sensitivity is paramount. To address these challenges, this study proposes the Enhanced Privacy-Preserving Blockchain-Enabled Federated Learning (EPP-BCFL) framework—a novel architecture that mixes blockchain technology, hybrid privacy mechanisms, and optimized communication strategies. The proposed system features a three-layer design: (1) the Edge Nodes Layer, where client devices perform local model training while retaining raw data; (2) the Federated Model Aggregation Layer, which securely aggregates encrypted updates using Differential Privacy and Secure Multi-Party Computation (SMPC); and (3) the Blockchain Network Layer, which guarantees tamper-proof auditability and trust through a lightweight Proof-of-Stake (PoS) consensus enhanced with Byzantine Fault Tolerance (BFT). Experimental evaluation on the CIFAR-10 dataset demonstrates that EPP-BCFL achieves 95.2% accuracy, significantly reduced communication overhead, and strong resilience against adversarial attacks. Comparative analysis with existing FL models highlights the proposed framework’s superior performance with respect to privacy preservation, computational efficiency, and robust security, making it well-suited for secure, scalable healthcare applications. Health sciences/Health care Health sciences/Health occupations Physical sciences/Engineering Blockchain Federated Learning Edge Analytics Electronic Health Records Privacy-Preserving Secure Healthcare Systems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Jun, 2025 Reviews received at journal 31 May, 2025 Reviewers agreed at journal 31 May, 2025 Reviewers agreed at journal 31 May, 2025 Reviewers agreed at journal 31 May, 2025 Reviews received at journal 30 May, 2025 Reviewers agreed at journal 29 May, 2025 Reviews received at journal 29 May, 2025 Reviewers agreed at journal 29 May, 2025 Reviewers agreed at journal 29 May, 2025 Reviewers agreed at journal 29 May, 2025 Reviewers agreed at journal 29 May, 2025 Reviewers agreed at journal 29 May, 2025 Reviewers invited by journal 29 May, 2025 Editor invited by journal 29 May, 2025 Editor assigned by journal 23 May, 2025 Submission checks completed at journal 22 May, 2025 First submitted to journal 16 May, 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. 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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-6678464","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463642110,"identity":"1c0c3a71-4131-479c-b769-6144dd2eaf01","order_by":0,"name":"Munusamy S","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Munusamy","middleName":"","lastName":"S","suffix":""},{"id":463642111,"identity":"2e80e34e-9b25-494d-b262-b451ba858bef","order_by":1,"name":"Jothi K R","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACxgYGNoYEIOQHMUGAjbAWZogWyQbGxgaitDAwMIPUJDAYHIBZQ1BDe/+xBw8Y0vKNbyS3P2CosWPgkyagk7HnMLtBAkOO5bYbiUCHHUtmYJM5QEDLjGQ2iQSGCgOzMweBWtgOMIC4xGkx7gFp+Ue8lhwDA/bGxgbGNmK09Bw2k0gwSDOQON7YOCOxL5mHoBbD9sZnkj8qkg34m9kffPjwzU5OfgYhLQ0g0gDKAyrmwa8eCOQJqhgFo2AUjIJRAACgjjv1AHsQ+AAAAABJRU5ErkJggg==","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jothi","middleName":"K","lastName":"R","suffix":""}],"badges":[],"createdAt":"2025-05-16 07:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6678464/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6678464/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-12225-x","type":"published","date":"2025-07-28T16:38:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88268488,"identity":"e2192e5b-76c1-4b1e-a2f2-f4553d234e7e","added_by":"auto","created_at":"2025-08-04 16:52:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":687793,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPTnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6678464/v1_covered_39da4db0-bc60-47da-af0f-d14bf3b3eaad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blockchain-Enabled Federated Learning with Edge Analytics for Secure and Efficient Electronic Health Records Management","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Blockchain, Federated Learning, Edge Analytics, Electronic Health Records, Privacy-Preserving, Secure Healthcare Systems","lastPublishedDoi":"10.21203/rs.3.rs-6678464/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6678464/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid adoption of Federated Learning (FL) in privacy-sensitive domains such as healthcare, IoT, and smart cities highlights its potential to enable collaborative machine learning without compromising data ownership. 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