BPPFL: A Blockchain-Based Framework for Privacy-Preserving Federated Learning

BPPFL: A Blockchain-Based Framework for Privacy-Preserving Federated Learning | 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 BPPFL: A Blockchain-Based Framework for Privacy-Preserving Federated Learning Muhammad Asad, Safa Otoum This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4662864/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Federated Learning (FL) offers a collaborative approach to training machine learning models while preserving data privacy. However, FL faces significant privacy and security challenges, such as identity disclosure and model inference attacks. To this end, we propose a novel Blockchain-Based Framework for Privacy-Preserving Federated Learning (BPPFL), which integrates threshold signature authentication and threshold Paillier encryption with blockchain technology. The BPPFL framework secures participant authentication and protects against internal and external threats, while the blockchain provides an immutable ledger for recording transactions and model updates, ensuring transparency and security. Experimental results show that our framework significantly reduces computation and communication overhead compared to existing methods while maintaining high model accuracy and robust privacy guarantees. Our framework enhances the security and trustworthiness of FL applications, making it suitable for domains like healthcare, finance, and the IoT. Federated Learning Blockchain Privacy-Preserving Pallier Encryption Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Sep, 2024 Reviews received at journal 15 Jul, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers invited by journal 08 Jul, 2024 Editor assigned by journal 01 Jul, 2024 Submission checks completed at journal 01 Jul, 2024 First submitted to journal 30 Jun, 2024 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-4662864","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329666128,"identity":"5871cdd5-c805-4ad8-a9ae-5145885e6464","order_by":0,"name":"Muhammad Asad","email":"data:image/png;base64,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","orcid":"","institution":"Zayed University","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Asad","suffix":""},{"id":329666129,"identity":"8c5ed8dd-b28f-4771-9941-0f0cd774735c","order_by":1,"name":"Safa Otoum","email":"","orcid":"","institution":"Zayed University","correspondingAuthor":false,"prefix":"","firstName":"Safa","middleName":"","lastName":"Otoum","suffix":""}],"badges":[],"createdAt":"2024-06-30 13:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4662864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4662864/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60918535,"identity":"a63077a2-1c80-4a5e-ab0d-a7e36b26354a","added_by":"auto","created_at":"2024-07-23 14:16:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1217000,"visible":true,"origin":"","legend":"","description":"","filename":"SourceFiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4662864/v1_covered_56a653cd-3fc7-4cd3-9b54-f7eccc698500.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"BPPFL: A Blockchain-Based Framework for Privacy-Preserving Federated Learning","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":"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":"Federated Learning, Blockchain, Privacy-Preserving, Pallier Encryption","lastPublishedDoi":"10.21203/rs.3.rs-4662864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4662864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Federated Learning (FL) offers a collaborative approach to training machine learning models while preserving data privacy. 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