Merging Subgroup Information to Supplement Personal Information for Personal Federated Learning via Model Clustering

Merging Subgroup Information to Supplement Personal Information for Personal Federated Learning via Model Clustering | 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 Merging Subgroup Information to Supplement Personal Information for Personal Federated Learning via Model Clustering Xuan Cai, Wenan Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4734541/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 Personalized federated learning represents a pivotal strategy for addressing the challenges posed by statistical heterogeneity in federated learning. Clients optimize their models by leveraging information from other clients through a global model. Despite clients' expectations of acquiring requisite information from global aggregation, this process inevitably leads to a loss of personalized information, particularly impacting clients with limited dataset. Consequently, the acquisition of sufficient personalized information to enhance local models becomes arduous, thereby compromising the efficacy of model personalization. In response to this challenge, we propose the Federal Merging Subgroup Information (FedMSI) method to augment personalized information in personalized federated learning. FedMSI leverages model clustering to delineate subgroup divisions of akin personalized models, aggregates cluster center models based on these divisions and enriches personalized information by incorporating subgroup information from the cluster center models. Experimental findings demonstrate that FedMSI surpasses nine state-of-the-art methods by 7.89% in terms of accuracy under identical data heterogeneity conditions, while exhibiting a 20.34% enhancement when the client data volume is small. Finally, through ablation experiments, this study reaffirms that the augmentation of personalized information through subgroup information significantly enhances the classification performance of personalized models. Federated Learning Personalization Model Clustering Merge Subgroup Information Statistical Heterogeneity 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. 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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-4734541","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337283852,"identity":"33bf1fbe-2dfb-4108-bd3f-270de200270e","order_by":0,"name":"Xuan Cai","email":"","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Cai","suffix":""},{"id":337283853,"identity":"10f9aa79-f806-4a73-ac2f-73507f50ecd1","order_by":1,"name":"Wenan Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDACCTBpkwDhsRGvJQ2ohZk0LYdJ0CI/u/mYNE/N+TyD2/0HGD6UHWbgn92AXwvjnGNp0jzHbhcb3DnMwDjj3GEGiTsH8Gthlsgxk85hu5244UYyAzNv22EGA4kE/FrYwFr+nYNo+UuMFh6Qlty2AxAtjMRokZBIS7b+25dcLHnnsMHBnnPpPBI3CGiRn5F88OaMb3Z5fLcbHz74UWYtxz+DgBYk+xgYDoBcSqx6BlicjoJRMApGwSjABACbqUD8g7z3AwAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Wenan","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-07-13 09:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4734541/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4734541/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63051669,"identity":"49469dec-4e7e-4eeb-a010-b96be929da71","added_by":"auto","created_at":"2024-08-22 14:14:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2050730,"visible":true,"origin":"","legend":"","description":"","filename":"MergingSubgroupInformationtoSupplementPersonalInformationforPersonalFederatedLearningviaModelClustering7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4734541/v1_covered_900ee8b4-d53c-4226-a4a8-816e32d2300a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Merging Subgroup Information to Supplement Personal Information for Personal Federated Learning via Model Clustering","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, Personalization, Model Clustering, Merge Subgroup Information, Statistical Heterogeneity","lastPublishedDoi":"10.21203/rs.3.rs-4734541/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4734541/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePersonalized federated learning represents a pivotal strategy for addressing the challenges posed by statistical heterogeneity in federated learning. 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