6G Network Slicing and Traffic Optimization Based on Federated Learning

6G Network Slicing and Traffic Optimization Based on Federated Learning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 20 January 2025 V1 Latest version Share on 6G Network Slicing and Traffic Optimization Based on Federated Learning Authors : Adel Alkhalil 0000-0003-3135-9174 [email protected] , Mohammed Altamimi , Abdulaziz Aljarwan 0009-0005-8559-4809 , Magdy Abdelrhman , Yaser Altameemi , and Aakash Ahmad 0000-0002-3198-9638 Authors Info & Affiliations https://doi.org/10.22541/au.173735730.05207687/v1 423 views 148 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The prominent feature of autonomous vehicles is collecting real-time data in the form of road images, video through on-board sensors and cameras. Such data is then deployed to optimize the vehicular traffic network. This paper proposes a novel framework for traffic data optimization and network slicing in 6G. The main idea is automatically get the training sample from the global model. Higher sample learning accuracy is improved by deploying knowledge distillation-based training mechanism. The traffic visual data privacy is preserved using adaptive differential method. Experimentations are performed using vehicle and other datasets. Simulations results show that the proposed method has superior performance as compared with existing methods. Supplementary Material File (6g network slicing and traffic optimization based on federated learning.docx) Download 686.10 KB Information & Authors Information Version history V1 Version 1 20 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords 6g ai iov network slicing traffic data analysis Authors Affiliations Adel Alkhalil 0000-0003-3135-9174 [email protected] University of Hail College of Computer Science and Engineering View all articles by this author Mohammed Altamimi University of Hail College of Computer Science and Engineering View all articles by this author Abdulaziz Aljarwan 0009-0005-8559-4809 University of Hail College of Computer Science and Engineering View all articles by this author Magdy Abdelrhman University of Ha'il View all articles by this author Yaser Altameemi University of Ha'il View all articles by this author Aakash Ahmad 0000-0002-3198-9638 Lancaster University School of Computing & Communications View all articles by this author Metrics & Citations Metrics Article Usage 423 views 148 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Adel Alkhalil, Mohammed Altamimi, Abdulaziz Aljarwan, et al. 6G Network Slicing and Traffic Optimization Based on Federated Learning. Authorea . 20 January 2025. DOI: https://doi.org/10.22541/au.173735730.05207687/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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