Fair Client Selection and Encrypted Aggregation: A Federated Learning Framework for Intrusion Detection in Resource-Constrained Networks

Fair Client Selection and Encrypted Aggregation: A Federated Learning Framework for Intrusion Detection in Resource-Constrained Networks | 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 Fair Client Selection and Encrypted Aggregation: A Federated Learning Framework for Intrusion Detection in Resource-Constrained Networks Rokaya Akter, Bian Naizheng, Irshad Ullah, Sudhanshu Singh, Abhishank Singh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6331407/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract With the increasing deployment of resource-constrained networks, the need for secure and efficient intrusion detection is more pressing than ever, given the challenges posed by the heterogeneity and limited resources of client devices. Federated learning (FL) offers a promising decentralized approach, yet it faces critical issues such as privacy risks, class imbalance, and client diversity, hindering reliable global model development. To address these challenges, we introduce an adaptive federated learning framework designed to enhance security, balance data distribution, and optimize intrusion detection in distributed environments. Our framework ensures privacy preservation through encrypted model training using Fully Homomorphic Encryption (FHE), mitigates data imbalance by applying the Synthetic Minority Over-sampling Technique (SMOTE) based on the client selection informed by the highest and lowest Earth Mover's Distance (EMD) scores, thereby improving model fairness by strategically balancing client representation. By integrating these techniques, it effectively overcomes key obstacles in federated learning, transforming it into a practical and robust cybersecurity solution. We validate our approach using CICDDoS2019 and UNSW-NB15, two benchmark datasets known for their complex attack scenarios and diverse network traffic. The results are compelling, our method outperforms traditional methods across key metrics, achieving precision, recall, F1-score, MSE, and FAR of 0.9699, 0.9818, 0.9741, 0.0464, and 0.0619 respectively on the UNSW-NB15 dataset, and 0.8127, 0.9886, 0.8963, 0.3984, and 0.1889 respectively on the CICDDoS2019 dataset, even in resource-constrained and privacy-constrained environments. Our findings demonstrate that adaptive learning, intelligent client selection, and privacy-aware model aggregation are essential for future-proofing cybersecurity in distributed networks. Federated Learning Homomorphic Encryption Intrusion Detection Privacy Scalability Cybersecurity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Sep, 2025 Reviews received at journal 15 Jul, 2025 Reviews received at journal 09 Jul, 2025 Reviewers agreed at journal 08 Jul, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers invited by journal 26 May, 2025 Editor assigned by journal 01 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 28 Mar, 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. 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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-6331407","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":462265044,"identity":"e58a84a5-32cf-4180-bbd0-ff93f14dd6a9","order_by":0,"name":"Rokaya Akter","email":"","orcid":"","institution":"Hunan University","correspondingAuthor":false,"prefix":"","firstName":"Rokaya","middleName":"","lastName":"Akter","suffix":""},{"id":462265046,"identity":"ff887afc-a4e1-436a-9185-7a0368c267a4","order_by":1,"name":"Bian 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