Federated Learning-Based Trust and Energy-Aware Routing in Fog–Cloud Computing Environments for the Internet of Things

Federated Learning-Based Trust and Energy-Aware Routing in Fog–Cloud Computing Environments for the 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 Article Federated Learning-Based Trust and Energy-Aware Routing in Fog–Cloud Computing Environments for the Internet of Things Fengchai Wang, Kun Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7871081/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The rapid convergence of Fog, Cloud, and Internet of Things (IoT) technologies has introduced a new era of distributed intelligence and real-time data processing. However, ensuring secure, reliable, and energy-efficient communication across heterogeneous and resource-constrained nodes remains a fundamental challenge. This paper introduces a novel framework entitled Federated Learning-Based Trust and Energy-Aware Routing (FL-TEAR), designed to enhance routing performance in hybrid Fog–Cloud–IoT environments through collaborative intelligence, adaptive trust management, and dynamic energy optimization. The FL-TEAR system replaces static trust evaluation with a federated learning paradigm, allowing IoT and fog nodes to cooperatively train a global trust–energy model without exposing raw data. Trust scores are continuously refined based on behavioral patterns, communication reliability, and residual energy, while routing paths are selected using a composite fitness function integrating trustworthiness, energy availability, latency, and link stability. The hierarchical architecture, spanning IoT, fog, and cloud layers, reduces communication overhead, supports scalability, and preserves privacy. Simulation results confirm that FL-TEAR significantly outperforms state-of-the-art baselines such as E-ODMA + AOMDV, TAGA, and EigenTrust, achieving 22% higher trust accuracy, 23% lower energy consumption, 19% greater packet delivery ratio, and 31% lower delay. These findings demonstrate that federated learning can effectively balance security, sustainability, and quality of service (QoS) in large-scale IoT ecosystems, establishing FL-TEAR as a viable pathway toward intelligent, secure, and energy-efficient next-generation networks. Physical sciences/Engineering Physical sciences/Mathematics and computing Fog–Cloud Collaboration Trust Management Energy-Aware Routing Context-Aware Computing Hybrid IoT Networks Reinforcement Learning Secure Data Transmission Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviews received at journal 06 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers invited by journal 06 Nov, 2025 Editor assigned by journal 22 Oct, 2025 Editor invited by journal 22 Oct, 2025 Submission checks completed at journal 19 Oct, 2025 First submitted to journal 19 Oct, 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. 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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-7871081","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":544140874,"identity":"cdb31921-7322-4386-abbe-e1bedf2fb3b5","order_by":0,"name":"Fengchai Wang","email":"","orcid":"","institution":"Chifeng University","correspondingAuthor":false,"prefix":"","firstName":"Fengchai","middleName":"","lastName":"Wang","suffix":""},{"id":544140875,"identity":"a2996441-6859-479b-948b-dfc8a9e94860","order_by":1,"name":"Kun 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However, ensuring secure, reliable, and energy-efficient communication across heterogeneous and resource-constrained nodes remains a fundamental challenge. This paper introduces a novel framework entitled Federated Learning-Based Trust and Energy-Aware Routing (FL-TEAR), designed to enhance routing performance in hybrid Fog\u0026ndash;Cloud\u0026ndash;IoT environments through collaborative intelligence, adaptive trust management, and dynamic energy optimization.\u003c/p\u003e\u003cp\u003eThe FL-TEAR system replaces static trust evaluation with a federated learning paradigm, allowing IoT and fog nodes to cooperatively train a global trust\u0026ndash;energy model without exposing raw data. Trust scores are continuously refined based on behavioral patterns, communication reliability, and residual energy, while routing paths are selected using a composite fitness function integrating trustworthiness, energy availability, latency, and link stability. The hierarchical architecture, spanning IoT, fog, and cloud layers, reduces communication overhead, supports scalability, and preserves privacy.\u003c/p\u003e\u003cp\u003eSimulation results confirm that FL-TEAR significantly outperforms state-of-the-art baselines such as E-ODMA\u0026thinsp;+\u0026thinsp;AOMDV, TAGA, and EigenTrust, achieving 22% higher trust accuracy, 23% lower energy consumption, 19% greater packet delivery ratio, and 31% lower delay. These findings demonstrate that federated learning can effectively balance security, sustainability, and quality of service (QoS) in large-scale IoT ecosystems, establishing FL-TEAR as a viable pathway toward intelligent, secure, and energy-efficient next-generation networks.\u003c/p\u003e","manuscriptTitle":"Federated Learning-Based Trust and Energy-Aware Routing in Fog–Cloud Computing Environments for the Internet of Things","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 05:32:04","doi":"10.21203/rs.3.rs-7871081/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-12T19:56:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T13:18:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88994412257516004544920794209380947114","date":"2025-11-09T07:13:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T14:08:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53156238686302555102908939906191992200","date":"2025-11-06T10:37:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-06T10:24:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T00:34:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-22T19:12:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-19T19:46:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-19T19:42:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb2a1d8e-32ca-424c-8f72-be219cbff7f3","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57894536,"name":"Physical sciences/Engineering"},{"id":57894537,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-22T16:05:46+00:00","versionOfRecord":{"articleIdentity":"rs-7871081","link":"https://doi.org/10.1038/s41598-025-32010-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-20 15:58:08","publishedOnDateReadable":"December 20th, 2025"},"versionCreatedAt":"2025-11-17 05:32:04","video":"","vorDoi":"10.1038/s41598-025-32010-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-32010-0","workflowStages":[]},"version":"v1","identity":"rs-7871081","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7871081","identity":"rs-7871081","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}