A Federated Meta-Learning Aided Intelligent Edge Framework by Using the Parameter Optimization Approach

A Federated Meta-Learning Aided Intelligent Edge Framework by Using the Parameter Optimization Approach | 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 Meta-Learning Aided Intelligent Edge Framework by Using the Parameter Optimization Approach Xiaofeng Zhu, Qiaosong Fan, Jiaqiang Peng, Yuwen Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6819153/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Oct, 2025 Read the published version in EURASIP Journal on Wireless Communications and Networking → Version 1 posted 5 You are reading this latest preprint version Abstract Edge intelligence can enable fast intelligent services by integrating edge computing with machine learning, thereby facilitating intelligent information processing for Internet of Things (IoT) devices on the edge. However, intelligent data processing at the edge may expose IoT devices to the risk of private information leakage. To mitigate this issue, we propose a federal meta-learning-aided data processing framework to cope with complex tasks in edge IoT networks. Unfortunately, communications between edge IoT devices and edge servers in federated frameworks incur significant overhead. To address this challenge, we propose a parameter optimization algorithm that alleviates communication costs between edge IoT devices and edge servers, thereby reducing classification errors induced by parameter optimization. Moreover, the convergence of the federated meta-learning method is derived, which theoretically confirms the feasibility of the proposed approach. Simulation results demonstrate that the error minimization-based quantization compression optimization algorithm can substantially enhance communication efficiency while incurring only negligible precision losses. Edge intelligence privacy protecting federated learning meta learning parameter optimization Full Text Cite Share Download PDF Status: Published Journal Publication published 15 Oct, 2025 Read the published version in EURASIP Journal on Wireless Communications and Networking → Version 1 posted Editorial decision: Major revision 06 Jul, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers invited by journal 11 Jun, 2025 Editor assigned by journal 09 Jun, 2025 First submitted to journal 09 Jun, 2025 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-6819153","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469637733,"identity":"78214aa8-e38a-42fb-af56-0128932406f6","order_by":0,"name":"Xiaofeng Zhu","email":"","orcid":"","institution":"Xinyu University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Zhu","suffix":""},{"id":469637734,"identity":"2bca0a0b-028b-4ffb-a580-4d504dffd0d3","order_by":1,"name":"Qiaosong Fan","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Qiaosong","middleName":"","lastName":"Fan","suffix":""},{"id":469637739,"identity":"b8b641a8-b050-4ccc-b71f-58ef7131afb8","order_by":2,"name":"Jiaqiang Peng","email":"","orcid":"","institution":"Xinyu University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqiang","middleName":"","lastName":"Peng","suffix":""},{"id":469637740,"identity":"d778dec3-021a-4083-8fc8-81dc5530b61d","order_by":3,"name":"Yuwen Qian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYFCCBCCuYGCGcNiI1nIGpIWZFC2MbSAGsVr427MTHxfOu8NucP78AYYPZYcZ+Gc34NcicebtZuOZ254xG9xIZmCcce4wg8SdAwSsuZG7TZp322GgFmYGZt62wwwGEgn4dciDtcwBajl/mIH5LzFaDMBaGoBaDiQzMDMSo8UQ5BeeY4eZJW8kGxzsOZfOI3GDgBa547kbH/PUHE7mO3/w4YMfZdZy/DMIaIGBZBBxAIh5iFMPBHZEqxwFo2AUjIKRBwDE20M1SmIK3AAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yuwen","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2025-06-04 10:07:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6819153/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6819153/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13638-025-02511-7","type":"published","date":"2025-10-15T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93956045,"identity":"a73de9e4-978e-4879-b473-e19cadf46fa9","added_by":"auto","created_at":"2025-10-20 16:09:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":673902,"visible":true,"origin":"","legend":"","description":"","filename":"IntelligentEdgeIoTnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6819153/v1_covered_e4c91fb5-0ac6-4c22-9b2c-3dbb920b097c.pdf"}],"financialInterests":"","formattedTitle":"A Federated Meta-Learning Aided Intelligent Edge Framework by Using the Parameter Optimization Approach","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"eurasip-journal-on-wireless-communications-and-networking","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jwcn","sideBox":"Learn more about [EURASIP Journal on Wireless Communications and Networking](http://jwcn-eurasipjournals.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jwcn/default.aspx","title":"EURASIP Journal on Wireless Communications and Networking","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Edge intelligence, privacy protecting, federated learning, meta learning, parameter optimization","lastPublishedDoi":"10.21203/rs.3.rs-6819153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6819153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Edge intelligence can enable fast intelligent services by integrating edge computing with machine learning, thereby facilitating intelligent information processing for Internet of Things (IoT) devices on the edge.\nHowever, intelligent data processing at the edge may expose IoT devices to the risk of private information leakage.\nTo mitigate this issue, we propose a federal meta-learning-aided data processing framework to cope with complex tasks in edge IoT networks.\nUnfortunately, communications between edge IoT devices and edge servers in federated frameworks incur significant overhead.\nTo address this challenge, we propose a parameter optimization algorithm that alleviates communication costs between edge IoT devices and edge servers, thereby reducing classification errors induced by parameter optimization.\nMoreover, the convergence of the federated meta-learning method is derived, which theoretically confirms the feasibility of the proposed approach. \nSimulation results demonstrate that the error minimization-based quantization compression optimization algorithm can substantially enhance communication efficiency while incurring only negligible precision losses.","manuscriptTitle":"A Federated Meta-Learning Aided Intelligent Edge Framework by Using the Parameter Optimization Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 06:39:50","doi":"10.21203/rs.3.rs-6819153/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-07-06T09:25:28+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-06-11T08:09:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-11T07:37:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T03:33:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"EURASIP Journal on Wireless Communications and Networking","date":"2025-06-09T04:07:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"eurasip-journal-on-wireless-communications-and-networking","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jwcn","sideBox":"Learn more about [EURASIP Journal on Wireless Communications and Networking](http://jwcn-eurasipjournals.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jwcn/default.aspx","title":"EURASIP Journal on Wireless Communications and Networking","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18307c73-cee8-41f1-9dd1-7a717638014f","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:03:07+00:00","versionOfRecord":{"articleIdentity":"rs-6819153","link":"https://doi.org/10.1186/s13638-025-02511-7","journal":{"identity":"eurasip-journal-on-wireless-communications-and-networking","isVorOnly":false,"title":"EURASIP Journal on Wireless Communications and Networking"},"publishedOn":"2025-10-15 15:58:07","publishedOnDateReadable":"October 15th, 2025"},"versionCreatedAt":"2025-06-13 06:39:50","video":"","vorDoi":"10.1186/s13638-025-02511-7","vorDoiUrl":"https://doi.org/10.1186/s13638-025-02511-7","workflowStages":[]},"version":"v1","identity":"rs-6819153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6819153","identity":"rs-6819153","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}