Accelerated Federated Learning Using Self-Adapting Bat Algorithm

Accelerated Federated Learning Using Self-Adapting Bat Algorithm | 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 Accelerated Federated Learning Using Self-Adapting Bat Algorithm Jie Wang, Chaochao Sun, Yuan Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4755684/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Federated learning (FL) is an advanced distributed machine learning (ML) framework designed to address issues related to data silos and data privacy. A significant challenge in FL is the non-independent and identically distributed (Non-IID) nature of client data, resulting in issues of slow convergence rate and low prediction accuracy for the model. To tackle these issues, we propose a FL scheme based on the bat algorithm (FedBat), leveraging the echolocation mechanism of bats to effectively balance global and local search capabilities and optimizing model weight updates through dynamic adjustments of the search strategy. FedBat also allows for adaptive parameter adjustments across various datasets. To mitigate the client drift issue, we extend FedBat by using Jensen-Shannon(JS) divergence to quantify the difference between local and global models. Clients decide whether to upload their local models based on this difference, aiming to enhance the global model's generalization capability and minimize communication overhead. Experimental results demonstrate that FedBat converges 5 times faster and enhances test accuracy by more than 40% compared to FedAvg. The extended FedBat effectively mitigates the decrease in the generalization performance of the global model and reduces communication costs by around 20%. Comparing FedPSO, FedGwo, and FedProx shows that FedBat demonstrates superior performance in terms of convergence rate and test accuracy. We derive the formula for the expected convergence rate of FedBat, analyze the impact of various parameters on FL performance, and establish the upper bound of FedBat to evaluate its model divergence. federated learning Non-IID data bat algorithm model convergence self-adapting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Sep, 2024 Reviews received at journal 11 Sep, 2024 Reviews received at journal 08 Sep, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviewers agreed at journal 25 Aug, 2024 Reviewers agreed at journal 24 Aug, 2024 Reviews received at journal 23 Aug, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers invited by journal 23 Aug, 2024 Editor assigned by journal 18 Jul, 2024 Submission checks completed at journal 18 Jul, 2024 First submitted to journal 17 Jul, 2024 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. 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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-4755684","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332113548,"identity":"231e1fcd-0712-432e-b357-91c3e682b467","order_by":0,"name":"Jie Wang","email":"","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Wang","suffix":""},{"id":332113549,"identity":"004ca318-55df-47d7-9ab6-42a624693636","order_by":1,"name":"Chaochao Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAn0lEQVRIiWNgGAWjYBADOTb29gOkaTHm4zmTQJqWxHkSDgbEKTVn7z3A+KPGLr1NgiGB4UfFNsJaLHvOJTDzHEvObZNuPMDYc+Y2YS0GN3LMfzM2HMhtkzmQwMzYRpwWA8afDQfS2SQSDIjXwsDbcCCBeC0wvxi2AQP5IFF+gYWYvHx7+8EHPyqIcRgDD4JzgLB6dC2jYBSMglEwCrACAGptN90Xea5hAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":true,"prefix":"","firstName":"Chaochao","middleName":"","lastName":"Sun","suffix":""},{"id":332113551,"identity":"6fc3bbf4-7bea-4cd5-bd19-094e17567c4d","order_by":2,"name":"Yuan Peng","email":"","orcid":"","institution":"Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2024-07-17 10:59:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4755684/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4755684/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62266059,"identity":"5e0aa610-4f7d-4fa3-99b7-0b1b3966ce72","added_by":"auto","created_at":"2024-08-12 09:24:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6026215,"visible":true,"origin":"","legend":"","description":"","filename":"latex.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4755684/v1_covered_6e8a30a6-418a-445c-8a04-d8811ba0dc4d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accelerated Federated Learning Using Self-Adapting Bat Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"federated learning, Non-IID data, bat algorithm, model convergence, self-adapting","lastPublishedDoi":"10.21203/rs.3.rs-4755684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4755684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Federated learning (FL) is an advanced distributed machine learning (ML) framework designed to address issues related to data silos and data privacy. 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