A Federated and Privacy-Preserving Architecture for Scalable Collaborative Spam Detection in Distributed Multi-Cloud Environments

A Federated and Privacy-Preserving Architecture for Scalable Collaborative Spam Detection in Distributed Multi-Cloud Environments | 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 A Federated and Privacy-Preserving Architecture for Scalable Collaborative Spam Detection in Distributed Multi-Cloud Environments Shanmuga Priya R, Yogesh Rajkumar R, Chellaswamy C This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7464969/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 The increasing prevalence of spam traffic poses major challenges for distributed and multi-cloud computing environments, particularly regarding scalability, workload coordination, and data privacy. Modern cluster-based infrastructures require secure and efficient mechanisms to enable collaborative spam detection across heterogeneous providers while preserving local autonomy. This paper introduces FMH-SCS, a federated and privacy-preserving distributed architecture that enables collaborative spam detection in multi-cloud cluster environments. FMH-SCS integrates Federated Learning with Secure Multi-Party Computation and Homomorphic Encryption, ensuring that sensitive email data remains local while only encrypted model updates are exchanged across computing clusters. To address communication bottlenecks in large-scale distributed training, FMH-SCS employs optimized aggregation protocols that balance security with communication efficiency. We evaluate FMH-SCS using the Enron and SpamAssassin benchmark datasets, analyzing accuracy, precision, recall, F1-score, training overhead, and communication costs in distributed setups. Experimental results show that FMH-SCS improves spam detection accuracy by up to 6.12% compared with state-of-the-art baselines, while also reducing communication and synchronization overhead across distributed clusters. These findings demonstrate that FMH-SCS provides a scalable, privacy-preserving, and computation-efficient solution for collaborative spam detection in modern cluster and distributed computing environments. federated learning multi-cloud environments privacy-preserving protocols spam detection homomorphic encryption 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. 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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-7464969","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515784921,"identity":"a6ca7990-6936-4ec8-a133-6e705d6c4121","order_by":0,"name":"Shanmuga Priya 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Environments","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":"federated learning, multi-cloud environments, privacy-preserving protocols, spam detection, homomorphic encryption","lastPublishedDoi":"10.21203/rs.3.rs-7464969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7464969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing prevalence of spam traffic poses major challenges for distributed and multi-cloud computing environments, particularly regarding scalability, workload coordination, and data privacy. Modern cluster-based infrastructures require secure and efficient mechanisms to enable collaborative spam detection across heterogeneous providers while preserving local autonomy. This paper introduces FMH-SCS, a federated and privacy-preserving distributed architecture that enables collaborative spam detection in multi-cloud cluster environments. FMH-SCS integrates Federated Learning with Secure Multi-Party Computation and Homomorphic Encryption, ensuring that sensitive email data remains local while only encrypted model updates are exchanged across computing clusters. To address communication bottlenecks in large-scale distributed training, FMH-SCS employs optimized aggregation protocols that balance security with communication efficiency. We evaluate FMH-SCS using the Enron and SpamAssassin benchmark datasets, analyzing accuracy, precision, recall, F1-score, training overhead, and communication costs in distributed setups. Experimental results show that FMH-SCS improves spam detection accuracy by up to 6.12% compared with state-of-the-art baselines, while also reducing communication and synchronization overhead across distributed clusters. These findings demonstrate that FMH-SCS provides a scalable, privacy-preserving, and computation-efficient solution for collaborative spam detection in modern cluster and distributed computing environments.\u003c/p\u003e","manuscriptTitle":"A Federated and Privacy-Preserving Architecture for Scalable Collaborative Spam Detection in Distributed Multi-Cloud Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 16:37:38","doi":"10.21203/rs.3.rs-7464969/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"3a024859-30b1-4de6-a5ac-b5311751d7ff","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-20T14:09:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 16:37:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7464969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7464969","identity":"rs-7464969","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}