An Energy-Aware Combinatorial Contextual Neural Bandit Approachfor Joint Performance Optimization in Client Selection for Federated Learning

An Energy-Aware Combinatorial Contextual Neural Bandit Approachfor Joint Performance Optimization in Client Selection for Federated Learning | 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 An Energy-Aware Combinatorial Contextual Neural Bandit Approachfor Joint Performance Optimization in Client Selection for Federated Learning Xiangyu Ma, Wei Shi, Junfeng Wen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5175921/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 In the evolving landscape of machine learning (ML), federated learning (FL) stands out as an innovative strategy for training models across dispersed devices without centralizing raw data. Such an approach, however, grapples with data heterogeneity challenges, violating the independent and identically distributed (IID) assumption and undermining the global model accuracy. To address this, we present federated adversary-resilient neural selector (FANS), a sophisticated context-aware client selection algorithm, leveraging a combinatorial contextual neural bandit framework. This algorithm accentuates the enhanced extraction of contextual information by evaluating each local client with a universally standardized dataset, subsequently yielding a more insightful contextual representation tailored for federated settings. Additionally, we further address another crucial aspect of client selection — energy consumption. Considering this key factor along with the global accuracy jointly, greatly increase the adaptability of FL in real-world applications. We then introduce the Selection Robustness Score (SRS), a novel metric designed to quantify the efficacy of client selection under both adversarial and energy-constrained conditions. Using this metric, we demonstrate FANS’s effectiveness in enhancing the FL process. Empirical evaluations across diverse settings reveal our method’s superiority over current state-of-the-art solutions, with significant improvement in the SRS and energy conservation. 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-5175921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376856220,"identity":"187d7fc3-fee1-4cd8-898e-8f7f32357aaf","order_by":0,"name":"Xiangyu Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBACxgYeCIMfRCQUgEjmBuK0SILUJRhAxBgO4NUE1WIAVkWMFub23mOPC9u2yRsfP5344YEBgzz/7IONjz9U3GHgb8eukbHnXLrxzLbbhtvO5G6WADrMcMa5xGaDA2eeMUicScCuZUaOmTRv223GbTd4N4C0JDCcYWyTONh2mAHExqfFfvMM3s0/QFrkzzC2/wBr4X+AV0viBgnebWBbDIC2MIC1SOCwpeeMmTTPudvJM87kbrNIMJAw3HiGsVnizJnDPBI3sNti2N4D1FJ227a//ezmmz8qbOTlzjAf/FBRcViOvx+7LYYNIKvY4HwJOIsHq3ogkAeTf3BJj4JRMApGwSgAAgDC8mJk0fgsdAAAAABJRU5ErkJggg==","orcid":"","institution":"Carleton University","correspondingAuthor":true,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Ma","suffix":""},{"id":376856221,"identity":"7e0d7323-2e23-4f35-a5ed-c7765b059382","order_by":1,"name":"Wei Shi","email":"","orcid":"","institution":"Carleton University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Shi","suffix":""},{"id":376856222,"identity":"2d0a3e11-583f-454f-a212-6d9420c1e6dd","order_by":2,"name":"Junfeng Wen","email":"","orcid":"","institution":"Carleton University","correspondingAuthor":false,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Wen","suffix":""}],"badges":[],"createdAt":"2024-09-29 16:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5175921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5175921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71112355,"identity":"eb07cf78-63b1-4f73-a685-3d8e0bb8e482","added_by":"auto","created_at":"2024-12-11 09:02:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23122735,"visible":true,"origin":"","legend":"","description":"","filename":"IJISnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5175921/v1_covered_43749d50-4b72-41a8-a8bd-a27ec5ec7fd1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Energy-Aware Combinatorial Contextual Neural Bandit Approachfor Joint Performance Optimization in Client Selection for Federated Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-5175921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5175921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the evolving landscape of machine learning (ML), federated learning (FL) stands out as an innovative strategy for training models across dispersed devices without centralizing raw data. 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