An Optimized FL-XAI model for secured and trustworthy candidate selection

An Optimized FL-XAI model for secured and trustworthy candidate selection | 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 Optimized FL-XAI model for secured and trustworthy candidate selection Siddhesh Fuladi, Nallakaruppan M. K., Malathy Sathyamoorthy, Balamurugan Balusamy, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4475624/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 Reliability and trustworthiness are the two pillars of decision support systems deployed in the selection process of automated candidates. The automation should ensure the selection's correctness and the decision's fairness. Conventional models work around fuzzy-based systems, exploiting multi-criteria decision support systems. Here, we propose a procedure combining the advantages of Federated Learning (FL) and Explainable Artificial Intelligence (XAI), ensuring privacy, reliability, and fairness in selecting candidates. We propose an architecture in which the exploitation of FL provides more accurate classification results while XAI provides a trustworthy and reliable representation of the candidate selection through decision plots. The SHAPELY model is used in the proposed work for explanation. Results and comparisons with several machine learning (ML) algorithms show the superiority of the proposed architecture. FL can reach an accuracy of 96%, thus confirming the validity of the proposed approach for providing an automated and well-explained candidate selection process. Candidate Selection Explainable AI Federated Learning RandomizedSearchCV Standard Scaler 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. 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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-4475624","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310701017,"identity":"c5da8abe-48d0-4295-b879-57bcfad76e77","order_by":0,"name":"Siddhesh Fuladi","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Siddhesh","middleName":"","lastName":"Fuladi","suffix":""},{"id":310701018,"identity":"7ed59015-0e7d-417e-9514-cb9d0055dcdc","order_by":1,"name":"Nallakaruppan M. 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The automation should ensure the selection's correctness and the decision's fairness. Conventional models work around fuzzy-based systems, exploiting multi-criteria decision support systems. Here, we propose a procedure combining the advantages of Federated Learning (FL) and Explainable Artificial Intelligence (XAI), ensuring privacy, reliability, and fairness in selecting candidates. We propose an architecture in which the exploitation of FL provides more accurate classification results while XAI provides a trustworthy and reliable representation of the candidate selection through decision plots. The SHAPELY model is used in the proposed work for explanation. Results and comparisons with several machine learning (ML) algorithms show the superiority of the proposed architecture. 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