Co-Evolutionary Mechanism Design for Federated Traffic Classification in Multi-ISP Edge–Cloud Clusters

Co-Evolutionary Mechanism Design for Federated Traffic Classification in Multi-ISP Edge–Cloud Clusters | 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 Co-Evolutionary Mechanism Design for Federated Traffic Classification in Multi-ISP Edge–Cloud Clusters Hai-Anh Tran, Truong X. Tran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8604074/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Federated learning enables multiple Internet service providers to collaboratively train traffic classification models without sharing raw traffic traces, which is attractive for privacy and business confidentiality in edge--cloud cluster deployments. However, incentive mismatches can motivate strategic behaviors such as low-effort training, selective participation, or subtle update bias, which degrade classification accuracy, fairness, and system stability. This paper proposes a co-evolutionary mechanism design framework that jointly optimizes coordinator policies and adaptive strategic Internet service provider behaviors. A population of coordinator mechanisms, including participation control, reward and penalty rules, auditing policies, and aggregation settings, is evolved via multi-objective search to balance classification utility with communication and computational overhead. In parallel, a population of strategic behaviors is evolved and expanded using a large language model that generates realistic tactics from observed system feedback. Experiments on federated traffic classification show that, under \((p_{\mathrm{str}}=0.4)\) , the proposed approach achieves a macro-F1 of \((0.876)\) and improves the worst-ISP accuracy to 82.1%, outperforming fixed-rule and robustness-only baselines while maintaining practical coordination overhead. Federated Traffic Classification Multi-ISP Networks Edge--Cloud Clusters Incentive-aware Learning Co-Evolutionary Optimization Large Language Models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 May, 2026 Reviews received at journal 21 Feb, 2026 Reviews received at journal 20 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 12 Feb, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 14 Jan, 2026 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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