Privacy-Preserving and Communication-Efficient Federated Learning for Cloud-Scale Distributed Intelligence

Privacy-Preserving and Communication-Efficient Federated Learning for Cloud-Scale Distributed Intelligence | 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 Privacy-Preserving and Communication-Efficient Federated Learning for Cloud-Scale Distributed Intelligence Heyao Liu, Yue Kang, Yuchen Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7866368/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 This study focuses on privacy protection and multi-party collaborative optimization in cloud computing environments. A federated learning framework is proposed, integrating differential privacy mechanisms and communication compression strategies. The framework adopts a layered architecture consisting of local computing nodes, a compression module, and a privacy-enhancing module. It enables global model training without exposing raw data, ensuring both model performance and data security. During the training process, the framework uses the federated averaging algorithm as the basis for global aggregation. A Gaussian noise perturbation mechanism is introduced to enhance the model's resistance to inference attacks. To address bandwidth limitations in practical cloud computing scenarios, a lightweight communication compression strategy is designed. This helps reduce the overhead and synchronization pressure caused by parameter exchange. The experimental design includes sensitivity analysis from multiple dimensions, such as network bandwidth constraints, client count variation, and data distribution heterogeneity. These experiments validate the adaptability and robustness of the proposed method under various complex scenarios. The results show that the method outperforms existing approaches in several key metrics, including accuracy, communication rounds, and model size. The proposed approach demonstrates strong engineering deployability and system-level security. It provides a novel technical path for building efficient and trustworthy distributed intelligent systems. Federated learning differential privacy communication compression cloud computing security Full Text Additional Declarations The authors declare no competing interests. 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. 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-7866368","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529956328,"identity":"48cd52a6-2180-4585-9af5-7cdff734992a","order_by":0,"name":"Heyao Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Heyao","middleName":"","lastName":"Liu","suffix":""},{"id":529956329,"identity":"5ca94698-ae06-4615-bff1-1863051a8fc9","order_by":1,"name":"Yue Kang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Kang","suffix":""},{"id":529956330,"identity":"0cae9896-efde-43e4-8597-7d668f4dc4c7","order_by":2,"name":"Yuchen Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYHACNiC24GFgbz784AOIy06cFgkeBp5jaYYzQFxmIrUAUY6BNA+IT0iLwY3kZw8+7pCQMThzxsDY5tc2eT5mBsYPH3PwaUkzN5x5RoLH4HhbwePcvtuGbcwMzJIzt+HTksMmzdsG1HLm8Abj3J7bjEAtbMy8hLT8BWm5kWAgbdlz2544LYxgLSkG0gw/bicS1CJ55pmZZC9Qi+QZYCD3NtxObmNmbMbrF77jyc8kfrbZ2PMdB0bljz+3bee3Nx/88BGPFoUDyDzGNjDZgFs9EMijSv/Bq3gUjIJRMApGKAAAVhRRO6V3HkcAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Yuchen","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-10-15 09:27:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7866368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7866368/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93654378,"identity":"99817892-9516-4dca-b25f-4cf96ed148d5","added_by":"auto","created_at":"2025-10-16 06:42:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":646654,"visible":true,"origin":"","legend":"","description":"","filename":"PrivacyPreservingandCommunicationEfficientFederatedLearningforCloudScaleDistributedIntelligence.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7866368/v1_covered_be791555-8a6c-4f44-8937-8fca9b7f6e0c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePrivacy-Preserving and Communication-Efficient Federated Learning for Cloud-Scale Distributed Intelligence\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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, differential privacy, communication compression, cloud computing security","lastPublishedDoi":"10.21203/rs.3.rs-7866368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7866368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eThis study focuses on privacy protection and multi-party collaborative optimization in cloud computing environments. 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