Towards a Dynamic Gradient Evaluation Strategy-based Federative Learning model

Towards a Dynamic Gradient Evaluation Strategy-based Federative Learning model | 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 Towards a Dynamic Gradient Evaluation Strategy-based Federative Learning model Konstantinos D. Stergiou, Konstantinos E. Psannis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5183315/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Federated Learning (FL) has emerged as a promising approach for training machine learning models on decentralized data, but it poses significant challenges in terms of model optimization and performance evaluation. To address these challenges, this paper introduces an application generator/tailoring tool designed specifically for FL analysts. The tool provides a framework for configuring Federated Averaging algorithms and tailoring them to suit a related set of applications, such as image recognition. The application generator/tailoring tool employs a dynamic gradient evaluation strategy to evaluate the performance of the FL model at runtime. This approach enables the analyst to adjust the FL model's behavior to achieve the desired objectives in terms of communication costs and data convergence requirements. By providing such flexibility, the tool empowers analysts to optimize FL models according to their specific needs and priorities. To demonstrate the effectiveness of the application generator/tailoring tool, the authors conducted experiments with traffic sign images. The results showed that the tool is highly effective in improving the performance of FL models for image recognition tasks. This finding suggests that the tool has significant potential to accelerate the development of FL models and enhance their performance in real-world applications. Overall, the application generator/tailoring tool presented in this paper provides a powerful and flexible approach to FL model optimization and evaluation. By enabling analysts to dynamically evaluate and optimize FL models, the tool has the potential to accelerate the adoption of FL in a wide range of real-world applications, from healthcare to transportation and beyond. Impact Statement — This paper presents a novel application generator/tailoring tool that allows Federated Learning analysts to dynamically evaluate and optimize FL models for image recognition tasks, while balancing communication costs and data convergence requirements. The tool's effectiveness is demonstrated through experiments with traffic sign images, highlighting its potential to accelerate FL development and improve performance in real-world applications. Adaptive Algorithms Application Generators Artificial General Intelligence Automatic Machine Learning Federated Averaging Federated Learning Mobile Edge Computing Traffic Sign Image Recognition Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 02 May, 2025 Reviewers invited by journal 02 May, 2025 Editor invited by journal 27 Apr, 2025 First submitted to journal 02 Oct, 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. 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-5183315","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450964684,"identity":"ebe65fda-dcaa-4cd9-82f0-fea276423531","order_by":0,"name":"Konstantinos D. Stergiou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACCTBZgcwhrIUZSJ4hWQtjGylaJPvPH3xcOe+wnG4D88HbPAy1+QS1SEskMxue3XbY2OwAW7I1D8NxywZCWuQkmNkkG7cdTtx2gMdMmofhmAFBW+T4D7P/bJwD0sL/jTgt0gzJbIyNDWBb2IBaaghrkZyRbCzZcCzd2Owwm7HlHIMDhLVInD/48GNDjbWc2fHmhzfeVNQR1oIAoChlMDhMgg4oqCNdyygYBaNgFAx7AAD8LTVtR4QiIgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5935-6349","institution":"University of Macedonia Department of Applied Informatics: Panepistemio Makedonias Tmema Epharmosmenes Plerophorikes","correspondingAuthor":true,"prefix":"","firstName":"Konstantinos","middleName":"D.","lastName":"Stergiou","suffix":""},{"id":450964685,"identity":"e995e1f5-6209-40e5-903f-72e8400a7a68","order_by":1,"name":"Konstantinos E. Psannis","email":"","orcid":"","institution":"University of Macedonia Department of Applied Informatics: Panepistemio Makedonias Tmema Epharmosmenes Plerophorikes","correspondingAuthor":false,"prefix":"","firstName":"Konstantinos","middleName":"E.","lastName":"Psannis","suffix":""}],"badges":[],"createdAt":"2024-09-30 20:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5183315/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5183315/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82212726,"identity":"aba4eafa-b4ec-4b11-b3a4-9eeb49e3d215","added_by":"auto","created_at":"2025-05-07 20:18:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":488994,"visible":true,"origin":"","legend":"","description":"","filename":"FederatedLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5183315/v1_covered_feadf517-e238-47d1-9558-2a4a1374b9b2.pdf"}],"financialInterests":"","formattedTitle":"Towards a Dynamic Gradient Evaluation Strategy-based Federative Learning model","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":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Adaptive Algorithms, Application Generators, Artificial General Intelligence, Automatic Machine Learning, Federated Averaging, Federated Learning, Mobile Edge Computing, Traffic Sign Image Recognition","lastPublishedDoi":"10.21203/rs.3.rs-5183315/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5183315/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFederated Learning (FL) has emerged as a promising approach for training machine learning models on decentralized data, but it poses significant challenges in terms of model optimization and performance evaluation. To address these challenges, this paper introduces an application generator/tailoring tool designed specifically for FL analysts. The tool provides a framework for configuring Federated Averaging algorithms and tailoring them to suit a related set of applications, such as image recognition. The application generator/tailoring tool employs a dynamic gradient evaluation strategy to evaluate the performance of the FL model at runtime. This approach enables the analyst to adjust the FL model's behavior to achieve the desired objectives in terms of communication costs and data convergence requirements. By providing such flexibility, the tool empowers analysts to optimize FL models according to their specific needs and priorities. To demonstrate the effectiveness of the application generator/tailoring tool, the authors conducted experiments with traffic sign images. The results showed that the tool is highly effective in improving the performance of FL models for image recognition tasks. This finding suggests that the tool has significant potential to accelerate the development of FL models and enhance their performance in real-world applications. Overall, the application generator/tailoring tool presented in this paper provides a powerful and flexible approach to FL model optimization and evaluation. By enabling analysts to dynamically evaluate and optimize FL models, the tool has the potential to accelerate the adoption of FL in a wide range of real-world applications, from healthcare to transportation and beyond.\u003c/p\u003e \u003cp\u003eImpact Statement \u0026mdash; This paper presents a novel application generator/tailoring tool that allows Federated Learning analysts to dynamically evaluate and optimize FL models for image recognition tasks, while balancing communication costs and data convergence requirements. The tool's effectiveness is demonstrated through experiments with traffic sign images, highlighting its potential to accelerate FL development and improve performance in real-world applications.\u003c/p\u003e","manuscriptTitle":"Towards a Dynamic Gradient Evaluation Strategy-based Federative Learning model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 19:46:38","doi":"10.21203/rs.3.rs-5183315/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-05-02T12:35:36+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-02T08:14:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Soft Computing","date":"2025-04-27T16:45:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Soft Computing","date":"2024-10-02T15:22:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e685f527-8c8e-4803-949a-904709604f48","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T19:46:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 19:46:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5183315","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5183315","identity":"rs-5183315","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}