Comparative Analysis of Privacy-Preserving Collaborative Learning Approaches: Security, Efficiency, and Convergence

Comparative Analysis of Privacy-Preserving Collaborative Learning Approaches: Security, Efficiency, and Convergence | 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 Comparative Analysis of Privacy-Preserving Collaborative Learning Approaches: Security, Efficiency, and Convergence Asmae BRIOUYA, Hasnae BRIOUYA, Ali CHOUKRI, Mohamed AMNAI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6067014/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 This study explores the comparative strengths of distributed learning models, Federated Learning (FL), Blind Federated Learning (BFL), Blended Blind Federated Learning (BBFL), Split Learning (SL), and Decentralized Learning (DL), by evaluating their performance metrics, convergence rates, and security features. Distributed learning models aim to leverage data from multiple clients while maintaining privacy. Each model uses different architectural and security mechanisms to achieve this, resulting in unique strengths and limitations in terms of scalability, resilience to attacks, and data integrity. By comparing convergence speed, accuracy, and resilience to specific threats, this paper provides insights into each model’s effectiveness for privacy-sensitive applications, particularly in handling model inversion, gradient leakage, and data poisoning. Results suggest that while DL exhibits superior performance and security, BBFL and SL models provide competitive alternatives for structured environments with moderate security needs. This analysis offers a framework to guide distributed learning model selection based on application requirements and security priorities. Distributed Learning Federated Learning Security Convergence Privacy 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. 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-6067014","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":422135690,"identity":"3269ac48-e060-4f36-b8f0-054b3d16b94a","order_by":0,"name":"Asmae BRIOUYA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYPCCAwxsYLqCgYdULWcYeIjVcwBCMbYxELaGv/904ocPDHfy+dh7zCR+zjssY8/A/PADY44dTi0SN3I3S85geGbZxnPGTLJ322Ggw9iMJRi3JeO25gbvBmkehsMGbBI5ZhK8YC0MZgyM25hx6pA/f3bz7z9QLZJ/54C0sH8DaqnHqcXgQO42aQaoFmneBpAWHpAth3FqMbyRu82yx+CZARvPsWJrmWPpPDyHeYolErcdx6lFDuiwGz8q7hjItzdvvPmmxtqevb1944eP26pxex/iPDDJIgGmQB5PIKABBpg/EKlwFIyCUTAKRhgAAPWMS6mlJJnwAAAAAElFTkSuQmCC","orcid":"","institution":"Université Ibn-Tofail","correspondingAuthor":true,"prefix":"","firstName":"Asmae","middleName":"","lastName":"BRIOUYA","suffix":""},{"id":422135691,"identity":"b1d649b9-a9be-4043-884a-5321c85eb2b3","order_by":1,"name":"Hasnae BRIOUYA","email":"","orcid":"","institution":"Université Ibn-Tofail","correspondingAuthor":false,"prefix":"","firstName":"Hasnae","middleName":"","lastName":"BRIOUYA","suffix":""},{"id":422135692,"identity":"ccf30a1e-6fc2-4463-b0dd-7a23e5f42b7c","order_by":2,"name":"Ali CHOUKRI","email":"","orcid":"","institution":"Université Ibn-Tofail","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"CHOUKRI","suffix":""},{"id":422135693,"identity":"d2e88de5-5893-4fe2-ad97-4da65f89392d","order_by":3,"name":"Mohamed AMNAI","email":"","orcid":"","institution":"Université Ibn-Tofail","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"AMNAI","suffix":""}],"badges":[],"createdAt":"2025-02-19 21:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6067014/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6067014/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81786683,"identity":"399709dd-4bbc-4956-a851-a06e8214737d","added_by":"auto","created_at":"2025-05-01 21:16:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":279263,"visible":true,"origin":"","legend":"","description":"","filename":"ComparativeAnalysisofPrivacyPreservingCollaborativeLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6067014/v1_covered_31c0d06d-eaf0-4283-b2bc-9a4ad5e352a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Analysis of Privacy-Preserving Collaborative Learning Approaches: Security, Efficiency, and Convergence","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":"Distributed Learning, Federated Learning, Security, Convergence, Privacy","lastPublishedDoi":"10.21203/rs.3.rs-6067014/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6067014/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study explores the comparative strengths of distributed learning models, Federated Learning (FL), Blind Federated Learning (BFL), Blended Blind Federated Learning (BBFL), Split Learning (SL), and Decentralized Learning (DL), by evaluating their performance metrics, convergence rates, and security features. 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