HyBloFED: A Hybrid Blockchain Integrated Federated Learning Approach for Brain Tumor Classification

HyBloFED: A Hybrid Blockchain Integrated Federated Learning Approach for Brain Tumor Classification | 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 HyBloFED: A Hybrid Blockchain Integrated Federated Learning Approach for Brain Tumor Classification Bela Shrimali, Sarthak Joshi, Hiren Patel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7008997/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 Brain tumors, complex and potentially devastating, demand precise classification for effective patient prognosis and treatment planning. This paper introduces a novel approach to automate brain tumor classification using deep learning techniques, particularly convolutional neural networks (CNNs). However, conventional centralized methods compromise patient privacy and data security. To address this issue, federated learning (FL), a collaborative paradigm enabling model training across multiple institutions and aggregating models at a central server while preserving the confidentiality of sensitive medical data, is proposed. Moreover, an aggregation function at the central server is modified to identify the effect of aggregation on global model training. In addition to that, Blockchain technology is also integrated with FL architecture to enhance privacy preservation and trust, to ensure the integrity and immutability of patient data. By synergistically integrating modified FL, CNNs, and Blockchain technology, the proposed approach achieves accuracy (98%) and security in brain tumor classification. Through this, it aims to advance the field of medical imaging while prioritizing patient privacy and data security (through Blockchain technology) in brain tumor diagnosis and treatment. Blockchain Security Brain tumor Federated learning Deep learning security integrity Full Text 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-7008997","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511271102,"identity":"e8ece653-2b5b-4354-82a5-2d5ac703b63f","order_by":0,"name":"Bela Shrimali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBACAwbmBiB1gIGNgYHxQEIFkM0MFsGnhRGuheFAwhmQFkYitYDAAcY2EEVAizl7Y+MHhj938vnEDj848HBebTR/O1DLj4ptOLVY9hxslmBse2bZJp1mcCBx2/HcGYcZGxh7ztzG7bAbiQ0SjA2HDdikE0BajuU2ALUwM7bh0XL/YfMPhj8gLekfDiTOOZY7n6CWG4xtEgxsIC05QFsaanI3ENJi2ZPYZpHYBtZScCDh2IHcjUAtB/H5xZz98OEbH4AOk5+dvvHhj5q63HnnDx988KMCtxYwSEAwD4PJA/jVo4I6UhSPglEwCkbBCAEA0jJjyEDzaCQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7543-5389","institution":"Nirma University Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Bela","middleName":"","lastName":"Shrimali","suffix":""},{"id":511271103,"identity":"80726bc6-02c7-457a-b6a5-eeefff242c5d","order_by":1,"name":"Sarthak Joshi","email":"","orcid":"","institution":"Nirma University Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sarthak","middleName":"","lastName":"Joshi","suffix":""},{"id":511271104,"identity":"48f03fdb-c660-4bb4-ab05-07b3bd38d2c4","order_by":2,"name":"Hiren Patel","email":"","orcid":"","institution":"Kadi Sarva Vishwavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Hiren","middleName":"","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2025-06-30 09:45:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7008997/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7008997/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94259082,"identity":"8ac55fa0-2d86-4984-a25a-7212c3dafe28","added_by":"auto","created_at":"2025-10-24 08:26:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":681317,"visible":true,"origin":"","legend":"","description":"","filename":"FLBlockchainBrainTumour3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7008997/v1_covered_71a1cb60-2359-498c-a9c1-b39a22c147f3.pdf"}],"financialInterests":"","formattedTitle":"HyBloFED: A Hybrid Blockchain Integrated Federated Learning Approach for Brain Tumor\nClassification","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":"Blockchain, Security, Brain tumor, Federated learning, Deep learning, security, integrity","lastPublishedDoi":"10.21203/rs.3.rs-7008997/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7008997/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Brain tumors, complex and potentially devastating, demand precise classification for effective patient prognosis and treatment planning. 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