ZDKD-FLID:Zero-Data Knowledge Distillation of Federated Learning for Intrusion Detection

ZDKD-FLID:Zero-Data Knowledge Distillation of Federated Learning for Intrusion Detection | 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 ZDKD-FLID:Zero-Data Knowledge Distillation of Federated Learning for Intrusion Detection TiaoKang Gao, XiaoNing Jin, Yingxu Lai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3423251/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Over the years, federated learning-based intrusion detection has attracted attention because it preserves data privacy and improves the detection capabilities of local models. However, the majority of existing methods in this domain are tailored for homogeneous models. Given the impact of factors such as hardware disparities and business requirements, local models often exhibit heterogeneity, which significantly restricts the development and application of federated learning for intrusion detection. Therefore, to address the challenge posed by model heterogeneous federated learning-based intrusion detection, this paper proposes a novel framework called zero-data knowledge distillation of federated learning for intrusion detection (ZDKD-FLID). This framework not only effectively addresses the issue of model heterogeneity but also operates without relying on a public dataset. Firstly, on the node side, the prediction model and local model perform knowledge distillation learning. Secondly, on the server side, the prediction model is selected and aggregated to generate a global prediction model. Additionally, a generator optimized with particle swarm optimization is employed for generative adversarial learning, enabling the generation of samples. Finally, the local model is trained using samples containing knowledge from other heterogeneous models, effectively improving the accuracy of intrusion detection. To validate its efficacy, ZDKD-FLID is compared with state-of-the-art algorithms on the CICIDS-2017 and UNSW-NB15 dataset. Consequently, ZDKD-FLID demonstrates superior performance compared to all the other considered algorithms. Federated learning Intrusion detection Heterogeneous models Knowledge distillation Generative adversarial network Particle swarm optimization Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major Revision 02 Oct, 2024 Reviewers agreed at journal 16 Sep, 2024 Reviewers invited by journal 28 May, 2024 Editor invited by journal 07 Feb, 2024 Editor assigned by journal 10 Oct, 2023 First submitted to journal 09 Oct, 2023 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. 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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-3423251","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307691277,"identity":"7fbdd2fa-3360-4f5c-ae10-84080c147bba","order_by":0,"name":"TiaoKang Gao","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-9790-4377","institution":"Beijing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"TiaoKang","middleName":"","lastName":"Gao","suffix":""},{"id":307691278,"identity":"872a9ca0-f352-4077-81e4-00b14641ca15","order_by":1,"name":"XiaoNing Jin","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"XiaoNing","middleName":"","lastName":"Jin","suffix":""},{"id":307691279,"identity":"eff21e4a-71f9-4711-a950-9de58842a5bf","order_by":2,"name":"Yingxu Lai","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yingxu","middleName":"","lastName":"Lai","suffix":""}],"badges":[],"createdAt":"2023-10-09 08:01:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3423251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3423251/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58028493,"identity":"474784e0-ca15-413c-9b30-fafd5138e5fd","added_by":"auto","created_at":"2024-06-10 07:18:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1054510,"visible":true,"origin":"","legend":"","description":"","filename":"ZDKDFLID.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3423251/v1_covered_b13f1573-4f19-46ef-9841-280a5310e720.pdf"}],"financialInterests":"","formattedTitle":"ZDKD-FLID:Zero-Data Knowledge Distillation of Federated Learning for Intrusion Detection","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":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Federated learning, Intrusion detection, Heterogeneous models, Knowledge distillation, Generative adversarial network, Particle swarm optimization","lastPublishedDoi":"10.21203/rs.3.rs-3423251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3423251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Over the years, federated learning-based intrusion detection has attracted attention because it preserves data privacy and improves the detection capabilities of local models. 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