Federated Learning over Serverless Edge Clusters: Balancing Privacy, Latency, and Trust

Federated Learning over Serverless Edge Clusters: Balancing Privacy, Latency, and Trust | 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 Federated Learning over Serverless Edge Clusters: Balancing Privacy, Latency, and Trust Poonam Verma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9597469/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 proliferation of Internet of Things (IoT) devices has driven the need for decentralized machine learning paradigms that preserve data privacy while minimizing transmission latency. Federated Learning (FL) has emerged as a robust solution, allowing distributed edge nodes to collaboratively train a global model without sharing raw local data. Concurrently, serverless computing offers a highly elastic, event-driven execution environment that eliminates the need for manual infrastructure provisioning. This paper proposes a novel framework that orchestrates Federated Learning pipelines over serverless edge clusters. By encapsulating local training tasks within ephemeral serverless functions, we achieve fine-grained resource scaling and cost efficiency. However, the transient nature of serverless execution introduces challenges in state management, synchronization, and trust verification across heterogeneous nodes. To address this, we formulate a mathematical model that optimizes the trade-off between communication latency, computational cost, and model convergence rates. Furthermore, we integrate a distributed trust mechanism to secure model weight updates against adversarial edge nodes. Extensive simulations demonstrate that our serverless FL framework reduces idle compute costs by up to 68% while maintaining convergence speeds within 5% of traditional, persistently provisioned edge clusters. Software Engineering Federated Learning Serverless Computing Edge Computing Distributed Trust Privacy-Preserving Machine Learning Resource Orchestration Full Text Additional Declarations The authors declare no competing interests. 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-9597469","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633513804,"identity":"951250c6-bac9-41ca-a833-938f457a0dd0","order_by":0,"name":"Poonam Verma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYLCDAx8q/ssBacYDDAwJROlgPDjjDLMxAwMzA9FamA/ztjEnNhDSYs7efOzBz5zDcubTDj8AamFL75fIPwB0YRoDf3s3Vn2WPcfSDXu3HTaWuZ1mcHDOOZ7cmTOSGYAuzGGQOHN2AzYtBjdyzCR4t6UlzpBOMDjwpkwid8ONZAagdRUMBkA2Li2Sf7el1c+QTv9wgIfNIN2AGC3SvNtsEiSkcwwO8rQlJEC15ODUAvRLmrTsNhvDGdI5BUAvHDCc2fPYAMhI48HlF1CISb7dJiEvIZ2++cOHigPy/OyJDx98qEiW42/vxe4wbIJgwINLAreWUTAKRsEoGAUwAABBK2n1dmxBpAAAAABJRU5ErkJggg==","orcid":"","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Poonam","middleName":"","lastName":"Verma","suffix":""}],"badges":[],"createdAt":"2026-05-03 04:57:58","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9597469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9597469/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108494167,"identity":"a25e5c43-9c4b-4eb5-a253-a53ad11754c4","added_by":"auto","created_at":"2026-05-05 10:02:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":119828,"visible":true,"origin":"","legend":"","description":"","filename":"Federated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9597469/v1_covered_73f2fe3f-7316-449a-8ec8-a8c9026c5f7c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFederated Learning over Serverless Edge Clusters:\u003c/p\u003e\n\u003cp\u003eBalancing Privacy, Latency, and Trust\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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, Serverless Computing, Edge Computing, Distributed Trust, Privacy-Preserving Machine Learning, Resource Orchestration","lastPublishedDoi":"10.21203/rs.3.rs-9597469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9597469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe proliferation of Internet of Things (IoT) devices has driven the need for decentralized machine learning paradigms that preserve data privacy while minimizing transmission latency. 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