FedVR360: Federated Learning enabled Privacy-Preservation for VR 360° Video Streaming in Vehicular Edge Computing

FedVR360: Federated Learning enabled Privacy-Preservation for VR 360° Video Streaming in Vehicular Edge Computing | 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 FedVR360: Federated Learning enabled Privacy-Preservation for VR 360° Video Streaming in Vehicular Edge Computing Shahbaz Khan, Jinling Zhang, Kamlesh Kumar Soothar, Weiwei Jiang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8808416/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract The integration of Virtual Reality (VR) 360° video streaming into Vehicular Edge Computing (VEC) enables immersive in-vehicle experiences but introduces significant challenges in privacy preservation and real-time performance. Existing predictive caching solutions rely on centralized learning, which requires aggregating sensitive user data such as head orientation and vehicle trajectories, thereby violating privacy regulations and exposing users to inference attacks. Federated Learning (FL) offers a promising solution; however, its deployment for VR streaming in VEC is constrained by highly non-Independent and Identically Distributed (non-IID) data, intermittent connectivity, and the need for joint multi-modal prediction without raw data exchange. To address these limitations, this paper proposes FedVR360, a comprehensive privacy-preserving FL framework for joint trajectory and viewport prediction in vehicular edge environments. The FedVR360 integrates a federated multi-modal Temporal Fusion Transformer with prototype-based cross-modal fusion, asynchronous hierarchical aggregation across vehicles and roadside units, and provides formal privacy guarantees using Rényi Differential Privacy. Additionally, a hybrid personalization strategy mitigates non-IID degradation. The performance evaluations conducted on real VR viewport traces and simulated vehicular trajectories show that FedVR360 achieves centralized performance by recovering over 84% of the centralized trajectory prediction gap and approximately 80% of the viewport prediction gap, while preserving strong privacy guarantees. Under a practical privacy budget, FedVR360 reduces membership inference attack success to near random guessing (50.5%) and significantly degrades the gradient inversion attacks. Across all evaluation metrics, FedVR360 achieves an F1@10 of 0.795 with corresponding improvements in precision and recall, reduces normalized trajectory prediction error to a mean absolute error of 0.282, maintains per-client prediction variance below 0.15 under non-IID data, and ensures real-time inference latency below 30 ms with moderate training and memory overhead, demonstrating a favorable balance between prediction accuracy, system efficiency, and formal privacy preservation. Privacy-Preserving Federated Learning VR 360 Video Streaming Vehicular Edge Computing Multi-Modal Learning Differential Privacy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Mar, 2026 Reviews received at journal 30 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 20 Feb, 2026 Editor assigned by journal 08 Feb, 2026 Submission checks completed at journal 08 Feb, 2026 First submitted to journal 06 Feb, 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. 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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-8808416","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594379484,"identity":"b5426edc-5c06-473b-87ee-7408d017fa9a","order_by":0,"name":"Shahbaz 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