Federated Learning-Based Cervical CancerClassification Using a Novel HybridKAN-ViT-Autoencoder Architecture

Federated Learning-Based Cervical CancerClassification Using a Novel HybridKAN-ViT-Autoencoder Architecture | 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 Federated Learning-Based Cervical CancerClassification Using a Novel HybridKAN-ViT-Autoencoder Architecture Mohammed Tawfik, Mohamed Heshmat, Islam S. Fathi, Ghazi Shakah, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8457292/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Cervical cancer remains a leading cause of cancer-related mortality among women globally, with early detection through automated cytology screening critical for improving patient outcomes. This study presents HybridKANViTAE, a novel federated learning framework for privacy-preserving cervical cancer classification that synergistically integrates three complementary architectural branches: an Autoencoder-CNN pathway for morphology-aware feature extraction, a Vision Transformer for global contextual modeling, and a Kolmogorov-Arnold Network with learnable activation functions. A learnable weighted fusion mechanism dynamically combines outputs from all branches, adapting to heterogeneous data distributions encountered in federated settings. Comprehensive evaluation on two benchmark datasets (SIPaKMeD and APCData) demonstrates robust performance with 99.68\% accuracy (MCC: 0.9960) and 91.69\% accuracy (MCC: 0.8616) respectively, while federated training achieves performance within 0.12-0.55\% of centralized approaches. Extensive ablation studies validate the contribution of each architectural component, with learnable fusion outperforming fixed fusion by 0.67-0.83 percentage points. Comprehensive explainable AI techniques including Grad-CAM, attention visualization, and branch contribution analysis ensure clinical interpretability by highlighting diagnostically relevant morphological features aligned with the Bethesda System criteria. The proposed framework addresses critical gaps in privacy-preserving collaborative learning for medical imaging, enabling multi-institutional model development while maintaining strict data confidentiality and regulatory compliance with HIPAA and GDPR. Results demonstrate that federated learning with sophisticated hybrid architectures provides a viable pathway for deploying AI-based cervical cancer screening systems in distributed clinical environments. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 13 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 12 Jan, 2026 First submitted to journal 26 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-8457292","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591200383,"identity":"1386de2e-3384-42a8-be81-8bfe5130251d","order_by":0,"name":"Mohammed Tawfik","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYNACGwkDfjCDjSjlzECcJmEg2UCiFgYDgwPEatFt4D/4mSfBwtj4/BkDhg9lhxkMjjfg12J2gJlZmidBwszsRo4B44xzQC1nDhDUwiCd+0PCxuwGjwEzbxtQy40Ewrb8zkmQsDHuP2PA/Bek5f4DglrYpIFazAwYcgyYGcG24NfBYHaY2cz6T4KEscSNtIKDPefSeSTPEHLY8cbHN2ck1Bn29x/e+OBHmbUc3/EDBKxhRmKD1PIoENKBCeQbSNYyCkbBKBgFwxwAAFqnP6P5hk3FAAAAAElFTkSuQmCC","orcid":"","institution":"Sana'a University","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Tawfik","suffix":""},{"id":591200386,"identity":"51546bd1-6fb9-4929-99fe-3e2a5d037e52","order_by":1,"name":"Mohamed Heshmat","email":"","orcid":"","institution":"Ajloun National University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Heshmat","suffix":""},{"id":591200387,"identity":"28cc0ec5-36d7-49a8-bc2c-c03ba60bbd8c","order_by":2,"name":"Islam S. 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