Cost-efficient hierarchical federated edge learning for satellite-terrestrial Internet of Things

Cost-efficient hierarchical federated edge learning for satellite-terrestrial Internet of Things | 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 Cost-efficient hierarchical federated edge learning for satellite-terrestrial Internet of Things Zhenjiang Zhang, Xintong Pei, Yaochen Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3962040/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 With the widespread deployment of dense Low Earth Orbit (LEO) constellations, satellites can serve as an alternative solution to the lack of proximal multi-access edge computing (MEC) servers for mobile Internet of Things (IoT) devices in remote areas. Simultaneously, the implementation of LEO on-board federated learning (FL), which can meet the data privacy requirements of user devices, makes it sensible to offloading data processing tasks to intelligent edge devices equipped with adaptive learning capabilities. However, traditional satellite on-board federated learning may encounter challenges due to the limited satellite resources. Hence, we propose a cost-efficient satellite-terrestrial assisted hierarchical federated edge learning (STA-HFEL) architecture and an innovative communication scheme between satellites based on Intra-plane ISLs in this paper. Accordingly, managing CPU resources for local training and allocating bandwidth for learning information upload among battery-limited mobile devices is crucial. With this in mind, we define a joint computation and communication resource optimization problem for device users to achieve global cost minimization. A distributed Jacobi-Proximal ADMM (JPADMM) algorithm is used to tackle the newly formulated problem iteratively. Extensive performance evaluations demonstrate that the potential of STA-HFEL as a cost-efficient and privacy-preserving approach for machine learning tasks across distributed remote environments. hierarchical federated learning satellite-terrestrial network multi-access edge computing resource allocation Intra-plane ISLs Jacobian Proximal ADMM 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. 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-3962040","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273800593,"identity":"478ae405-9127-43db-8b21-8e10a3ef6669","order_by":0,"name":"Zhenjiang Zhang","email":"data:image/png;base64,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","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Zhenjiang","middleName":"","lastName":"Zhang","suffix":""},{"id":273800594,"identity":"cd28b416-f085-449a-9d74-a54966580655","order_by":1,"name":"Xintong Pei","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xintong","middleName":"","lastName":"Pei","suffix":""},{"id":273800595,"identity":"70cf3efd-47c7-47a4-87b6-a4a03f919aa0","order_by":2,"name":"Yaochen Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yaochen","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-02-16 18:29:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3962040/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3962040/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51422581,"identity":"35ffc28b-edad-478b-9895-b049e58988d8","added_by":"auto","created_at":"2024-02-21 09:44:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":555147,"visible":true,"origin":"","legend":"","description":"","filename":"CostefficienthierarchicalfederatededgelearningforsatelliteterrestrialInternetofThings8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3962040/v1_covered_63fb4b24-c3e0-4aff-a17d-f853830f5276.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost-efficient hierarchical federated edge learning for satellite-terrestrial Internet of Things","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"hierarchical federated learning, satellite-terrestrial network, multi-access edge computing, resource allocation, Intra-plane ISLs, Jacobian Proximal ADMM","lastPublishedDoi":"10.21203/rs.3.rs-3962040/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3962040/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the widespread deployment of dense Low Earth Orbit (LEO) constellations, satellites can serve as an alternative solution to the lack of proximal multi-access edge computing (MEC) servers for mobile Internet of Things (IoT) devices in remote areas. Simultaneously, the implementation of LEO on-board federated learning (FL), which can meet the data privacy requirements of user devices, makes it sensible to offloading data processing tasks to intelligent edge devices equipped with adaptive learning capabilities. However, traditional satellite on-board federated learning may encounter challenges due to the limited satellite resources. Hence, we propose a cost-efficient satellite-terrestrial assisted hierarchical federated edge learning (STA-HFEL) architecture and an innovative communication scheme between satellites based on Intra-plane ISLs in this paper. Accordingly, managing CPU resources for local training and allocating bandwidth for learning information upload among battery-limited mobile devices is crucial. With this in mind, we define a joint computation and communication resource optimization problem for device users to achieve global cost minimization. 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