LCSA-Fed: A low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction

LCSA-Fed: A low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction | 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 LCSA-Fed: A low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction Lingru Cai, Yuelong Liu, Jianlong Xu, Mengqing Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4359561/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract As a distributed training method, federated learning (FL) has been widely used in the field of quality-of-service (QoS) prediction. However, existing FL-based QoS prediction methods ignore the unreliability of end devices, which will lead to wasted training resources and high communication costs. Considering that the instability of end devices in real training environments, we propose a low cost semi-asynchronous federated learning method (LCSA-Fed) based on lag tolerance to overcome the lower convergence rate and suboptimal prediction accuracy of models. LCSA-Fed is able to reduce model communication costs and training costs by tolerating relatively lagging local models. At the same time, we employ innovations in both the user selection phase and the model aggregation phase to improve prediction accuracy while reducing overheads. By conducting relevant validation experiments on a publicly available QoS dataset, we conclude that our model LCSA-Fed can effectively reduce overhead and improve prediction accuracy. distributed training QoS prediction federated learning lag tolerance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Jun, 2024 Reviews received at journal 31 May, 2024 Reviews received at journal 31 May, 2024 Reviews received at journal 27 May, 2024 Reviews received at journal 25 May, 2024 Reviewers agreed at journal 22 May, 2024 Reviewers agreed at journal 21 May, 2024 Reviewers agreed at journal 21 May, 2024 Reviewers agreed at journal 21 May, 2024 Reviewers invited by journal 21 May, 2024 Editor assigned by journal 05 May, 2024 Submission checks completed at journal 02 May, 2024 First submitted to journal 02 May, 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-4359561","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":298810589,"identity":"09d487ac-7448-4060-a206-9a37bf0f479f","order_by":0,"name":"Lingru Cai","email":"","orcid":"","institution":"Shantou University","correspondingAuthor":false,"prefix":"","firstName":"Lingru","middleName":"","lastName":"Cai","suffix":""},{"id":298810592,"identity":"e6c3de34-c578-4f0d-9414-77d70220a500","order_by":1,"name":"Yuelong Liu","email":"","orcid":"","institution":"Shantou University","correspondingAuthor":false,"prefix":"","firstName":"Yuelong","middleName":"","lastName":"Liu","suffix":""},{"id":298810596,"identity":"20f22315-15e4-46b3-8a02-6b4926f580fd","order_by":2,"name":"Jianlong Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3RMYvCMBTA8SeF3qJ2fUWsXyFSuFPwwzxxdXASNwUhk+71OzjcJDemdHDp6ZpRcRaUTg6CSeucdjy4/JeXQH6kJQA229+MBEBPTUcUe1GNoJouFacrEJ0mdVaNMEln8fjB4MvbZtmdQ9CUVMsmBuJHRPE6xbAfXXcYcwh9SU4rMhAPiUSD4/Bb/u5AEbUg16kbiKtI/MxJerkpMi8l+pYkv+W4Av1hxMqIvzpR0uYYMul+YnrA7iY9L1smwvbj0f3KBwE7JpfbbDroNPejODMR9RxUTMynfqDawggAPsT7r0TJQZvNZvu3vQDFolPPfCB8iQAAAABJRU5ErkJggg==","orcid":"","institution":"Shantou University","correspondingAuthor":true,"prefix":"","firstName":"Jianlong","middleName":"","lastName":"Xu","suffix":""},{"id":298810601,"identity":"bd820a29-4066-40ce-b8b8-fa1ff51bc3f5","order_by":3,"name":"Mengqing Jin","email":"","orcid":"","institution":"Shantou University","correspondingAuthor":false,"prefix":"","firstName":"Mengqing","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2024-05-02 13:59:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4359561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4359561/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56233420,"identity":"ef389a59-b21c-4c31-8905-ca95c5db5b35","added_by":"auto","created_at":"2024-05-10 08:05:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":409502,"visible":true,"origin":"","legend":"","description":"","filename":"LCSAFedClusterComputin.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4359561/v1_covered_331754e3-1211-4c7c-8c69-fcc2c551d0d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"LCSA-Fed: A low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction","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":"distributed training, QoS prediction, federated learning, lag tolerance","lastPublishedDoi":"10.21203/rs.3.rs-4359561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4359561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As a distributed training method, federated learning (FL) has been widely used in the field of quality-of-service (QoS) prediction. 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