Quantum Safe Federated Reinforcement Learning for Intelligent Energy Efficiency Optimization in IoT Enabled Smart Grids

Quantum Safe Federated Reinforcement Learning for Intelligent Energy Efficiency Optimization in IoT Enabled Smart Grids | 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 Quantum Safe Federated Reinforcement Learning for Intelligent Energy Efficiency Optimization in IoT Enabled Smart Grids Abdulatif Alabdulatif This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8815125/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract A smart grid is as an electricity system that manages digital and refined mechanism to monitor and handle the transfer of electricity from various sources, optimizing efficiency and reliability while reducing costs and environmental impacts. The rapid growth of smart grids raises total energy demand and renewable energy combination for smart, adaptive, and energy-efficient resource distribution strategies. Traditional energy management methods, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy consumption. But main challenges in smart grids are optimizing energy efficiency and controlling electricity generation, transmission, and distribution. This paper introduces an AI-powered approach to optimize energy in smart grids using Generalized Linear Regressive Quantum-Safe Federated Reinforcement Learning (GLRQS-FRL). The main aim of GLRQS-FRL model is to perform the energy efficiency optimization in IoT-enabled smart grids with minimal computation and communication overhead. To begin with, GLRQS-FRL model collects the smart grid data from the dataset. After the acquisition phase, data preprocessing is carried out to transform the raw dataset into cleaned format based on missing data handling and outlier removal. Followed by, two phase linear regression is employed to determine the most relevant features and remove the others. Finally, the Quantum Federated Reinforcement Learning performs the optimal energy usage prediction by employing Azadkia-Chatterjee correlation coefficient with the selected relevant features with higher accuracy. In addition, Quantum differential privacy model is employed to further protect sensitive data. Finally, accurate energy efficiency optimization results are predicted with minimal error. Experimental consideration of proposed GLRQS-FRL model is conducted using various evaluation metrics such as accuracy, RMSE, NMSE, R 2 score, computation overhead and communication overhead. The quantitatively analyzed results reveal that the proposed GLRQS-FRL model attains higher accuracy in smart grid optimization with minimal overhead as well as lesser error compared to traditional deep learning methods. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Smart grid optimization two phase linear regression Federated Reinforcement learning Azadkia-Chatterjee correlation coefficient Quantum-differential privacy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviews received at journal 16 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers invited by journal 10 Mar, 2026 Editor invited by journal 10 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 07 Feb, 2026 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-8815125","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":603863079,"identity":"87a5b0bf-e57f-4d4b-9b42-0088134bf9a3","order_by":0,"name":"Abdulatif Alabdulatif","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie2RsWrDMBCGzwiURZDVENy8QoyhtCRpX+WEwXu3DsUIAumWuaV5CD2CwkG7tavAHtwlXTokS/BUKi0ZCmro1kEfCI5Dn/47BBCJ/EdSdzowiRq4AmezY/N3Bb3CIFFYVX9VoKLTyvBJJTu8azNN7KPr8C3Thm0aAfU4GNIaluLzttDEc4XYFNrwciqAchVyLEKKnKQmyJXsG6mNOB8JP2rAGFtkPX55ZbB3Ka9OGR6cUl+HlIlFnsqlV4QfzPgU7hQmQ0rebpYXcrUtHkncPCCWruDF5XpCZUg5axZkd4c2W73c632PV65YvNvP23oeXB9+rOk/yA8cvH/yhUgkEokAfANQi2Tl8+ZewQAAAABJRU5ErkJggg==","orcid":"","institution":"Qassim University","correspondingAuthor":true,"prefix":"","firstName":"Abdulatif","middleName":"","lastName":"Alabdulatif","suffix":""}],"badges":[],"createdAt":"2026-02-07 11:38:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8815125/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8815125/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104781898,"identity":"32f48f64-21e6-4dfe-8893-c6d62d2e382f","added_by":"auto","created_at":"2026-03-17 07:56:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1596928,"visible":true,"origin":"","legend":"","description":"","filename":"QuantumSafeFederatedReinforcementLearningforsmartgridoptimizationmanucript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8815125/v1_covered_50987335-2cca-413c-835b-214d0e628db5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantum Safe Federated Reinforcement Learning for Intelligent Energy Efficiency Optimization in IoT Enabled Smart Grids","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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},"keywords":"Smart grid optimization, two phase linear regression, Federated Reinforcement learning, Azadkia-Chatterjee correlation coefficient, Quantum-differential privacy","lastPublishedDoi":"10.21203/rs.3.rs-8815125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8815125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA smart grid is as an electricity system that manages digital and refined mechanism to monitor and handle the transfer of electricity from various sources, optimizing efficiency and reliability while reducing costs and environmental impacts. The rapid growth of smart grids raises total energy demand and renewable energy combination for smart, adaptive, and energy-efficient resource distribution strategies. Traditional energy management methods, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy consumption. But main challenges in smart grids are optimizing energy efficiency and controlling electricity generation, transmission, and distribution. This paper introduces an AI-powered approach to optimize energy in smart grids using Generalized Linear Regressive Quantum-Safe Federated Reinforcement Learning (GLRQS-FRL). The main aim of GLRQS-FRL model is to perform the energy efficiency optimization in IoT-enabled smart grids with minimal computation and communication overhead. To begin with, GLRQS-FRL model collects the smart grid data from the dataset. After the acquisition phase, data preprocessing is carried out to transform the raw dataset into cleaned format based on missing data handling and outlier removal. Followed by, two phase linear regression is employed to determine the most relevant features and remove the others. Finally, the Quantum Federated Reinforcement Learning performs the optimal energy usage prediction by employing Azadkia-Chatterjee correlation coefficient with the selected relevant features with higher accuracy. In addition, Quantum differential privacy model is employed to further protect sensitive data. Finally, accurate energy efficiency optimization results are predicted with minimal error. Experimental consideration of proposed GLRQS-FRL model is conducted using various evaluation metrics such as accuracy, RMSE, NMSE, R\u003csup\u003e2\u003c/sup\u003e score, computation overhead and communication overhead. 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