PSO-Driven Client Selection for Federated Learning in IDS applications

PSO-Driven Client Selection for Federated Learning in IDS applications | 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 PSO-Driven Client Selection for Federated Learning in IDS applications Aymen WAli, Maher Boughdiri, Hichem Mrabet, Abderrazek Jemai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8389432/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Federated Learning has emerged as a highly promising distributed and collaborative learning paradigm, enabling local clients to train models without sharing their raw data. However, the global model’s performance is often impacted by heterogeneity and variability at the client level, making the selection of clients in each training round a critical factor for overall model effectiveness.In this paper, we propose a novel client selection strategy within the federated learning framework that leverages Particle Swarm Optimization (PSO) to identify the most suitable participants for training a high-performing model in fewer communication rounds. The PSO-based algorithm dynamically adjusts the contribution weights of client models based on their performance and stability metrics, aiming to improve global model accuracy, accelerate convergence, and enable smarter, adaptive model aggregation.We apply this approach specifically to develop a collaborative intrusion detection system for cybersecurity in Edge IIoT environments. Clients are selected based on key criteria such as data quality and individual model performance. Experimental evaluation on a real-world Edge IIoT dataset demonstrates that our solution achieves over 91% accuracy in the global model while reducing the number of communication rounds by approximately 40% compared to the traditional Federated Averaging (FedAvg) method.These results highlight that the proposed approach not only significantly boosts the accuracy of the global model but also accelerates its convergence, making it a robust and efficient solution for Intrusion Detection Solutions (IDS) applications. PSO Federated Learning Client Selection IDS IIoT Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Feb, 2026 Reviews received at journal 26 Jan, 2026 Reviews received at journal 26 Jan, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviews received at journal 15 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers invited by journal 14 Jan, 2026 Editor assigned by journal 09 Jan, 2026 Submission checks completed at journal 18 Dec, 2025 First submitted to journal 17 Dec, 2025 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-8389432","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576293142,"identity":"9086cf50-f09b-4045-99bd-73ec340b2d1b","order_by":0,"name":"Aymen WAli","email":"","orcid":"","institution":"SERCOM Laboratory, University of Carthage, Carthage","correspondingAuthor":false,"prefix":"","firstName":"Aymen","middleName":"","lastName":"WAli","suffix":""},{"id":576293143,"identity":"683c2189-71e8-4fa7-8700-fd4971849200","order_by":1,"name":"Maher 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