A Federated Cascade Learning Approach for Efficient Occupancy Detection in Smart Buildings

A Federated Cascade Learning Approach for Efficient Occupancy Detection in Smart Buildings | 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 A Federated Cascade Learning Approach for Efficient Occupancy Detection in Smart Buildings Mohamed Rafik Aymene Berkani, Ammar Chouchane, Yassine Himeur, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7441685/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 Occupancy detection plays a pivotal role in optimizing energy consumption, enhancing occupant comfort, and enabling intelligent decision-making in smart building environments. While traditional centralized learning methods often compromise user privacy and scalability, federated learning (FL) offers a promising privacy-preserving alternative. In this study, we propose Federated Cascade Learning with Long Short-Term Memory and Attention (FCL-LSTMA), a novel decentralized architecture that synergistically combines FL and cascade learning to enable accurate and efficient occupancy detection across distributed environments. The FCL-LSTMA framework operates through a two-stage training process: initially, Block B1 leverages 1D convolution, batch normalization, and a parallel LSTM layer for foundational feature learning. Once stabilized, the block is frozen and extended to Block B2, which incorporates attention mechanisms and an additional LSTM layer to refine temporal feature extraction. This progressive training ensures improved stability, model depth, and generalization without compromising data privacy. Experimental results on four benchmark datasets demonstrate the superiority of the proposed framework. FCL-LSTMA achieves outstanding accuracy for binary occupancy detection: 99.99% on the Living Room dataset, 98.95% on the UCI Occupancy Detection dataset, 99.95% on both the UCI Room Occupancy Estimation and IEQ datasets. For multi-class tasks, the model achieves 99.98% and 99.95% on the Living Room and UCI Room Occupancy Estimation datasets, respectively. Furthermore, it demonstrates computational efficiency with inference times ranging from 0.2028 to 0.562 milliseconds and memory usage between 0.17 MB and 0.49 MB. These results confirm the robustness, scalability, and privacy-preserving capabilities of FCL-LSTMA, making it a compelling solution for real-time occupancy detection in smart building systems. Physical sciences/Engineering Physical sciences/Mathematics and computing 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. 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-7441685","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":515618845,"identity":"939a0d1b-e101-4dd9-8656-08dc745807ef","order_by":0,"name":"Mohamed Rafik Aymene Berkani","email":"","orcid":"","institution":"University Yahia Fares of Medea","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Rafik Aymene","lastName":"Berkani","suffix":""},{"id":515618846,"identity":"e9f52f27-c7df-4323-863a-1ec780945d1d","order_by":1,"name":"Ammar Chouchane","email":"","orcid":"","institution":"University Center of 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While traditional centralized learning methods often compromise user privacy and scalability, federated learning (FL) offers a promising privacy-preserving alternative. In this study, we propose Federated Cascade Learning with Long Short-Term Memory and Attention (FCL-LSTMA), a novel decentralized architecture that synergistically combines FL and cascade learning to enable accurate and efficient occupancy detection across distributed environments. The FCL-LSTMA framework operates through a two-stage training process: initially, Block B1 leverages 1D convolution, batch normalization, and a parallel LSTM layer for foundational feature learning. Once stabilized, the block is frozen and extended to Block B2, which incorporates attention mechanisms and an additional LSTM layer to refine temporal feature extraction. This progressive training ensures improved stability, model depth, and generalization without compromising data privacy. Experimental results on four benchmark datasets demonstrate the superiority of the proposed framework. FCL-LSTMA achieves outstanding accuracy for binary occupancy detection: 99.99% on the Living Room dataset, 98.95% on the UCI Occupancy Detection dataset, 99.95% on both the UCI Room Occupancy Estimation and IEQ datasets. For multi-class tasks, the model achieves 99.98% and 99.95% on the Living Room and UCI Room Occupancy Estimation datasets, respectively. Furthermore, it demonstrates computational efficiency with inference times ranging from 0.2028 to 0.562 milliseconds and memory usage between 0.17 MB and 0.49 MB. These results confirm the robustness, scalability, and privacy-preserving capabilities of FCL-LSTMA, making it a compelling solution for real-time occupancy detection in smart building systems.","manuscriptTitle":"A Federated Cascade Learning Approach for Efficient Occupancy Detection in Smart Buildings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 06:11:44","doi":"10.21203/rs.3.rs-7441685/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"e5afdcaa-c700-49b7-ae97-bc5176d81c8e","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54772060,"name":"Physical sciences/Engineering"},{"id":54772061,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-19T14:24:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 06:11:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7441685","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7441685","identity":"rs-7441685","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}