not-yet-known not-yet-known not-yet-known unknown SLSVD: A Swarm Learning with Singular Value Decomposition for Industrial Digital Twins

not-yet-known not-yet-known not-yet-known unknown SLSVD: A Swarm Learning with Singular Value Decomposition for Industrial Digital Twins | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 27 March 2025 V1 Latest version Share on not-yet-known not-yet-known not-yet-known unknown SLSVD: A Swarm Learning with Singular Value Decomposition for Industrial Digital Twins Authors : Xingjia Wei , Pengcheng Zhao 0000-0002-0550-5804 [email protected] , Yatong Wang , and Shibao Sun Authors Info & Affiliations https://doi.org/10.22541/au.174305177.71186360/v1 202 views 95 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In the Industrial Internet of Things-Digital Twin (IIoT-DT), real-time mapping and collaborative modeling between physical entities and twins are achieved. The high concurrent transmission of massive sensor data from industrial electronic equipment leads to communication delays. Meanwhile, multimodal data has the characteristic of non-independent and identically distributed (non-IID). There are difficult to integrate. This letter proposes DT-SL framework in IIoT, which uses fully decentralized swarm learning (SL) to improve the efficiency of updating local twin models. We designed weighted multi-matrix singular value decomposition (SVD) to improve the consistency and efficiency of multimodal data fusion in the framework. Experimental results show that the proposed method outperforms the baseline method in model communication overhead, convergence rate and performance. Supplementary Material File (electronics+lettersmain document.docx) Download 1.69 MB Information & Authors Information Version history V1 Version 1 27 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ad hoc networks artificial intelligence cloud computing Authors Affiliations Xingjia Wei Henan University of Science and Technology View all articles by this author Pengcheng Zhao 0000-0002-0550-5804 [email protected] Henan University of Science and Technology View all articles by this author Yatong Wang Chinese Academy of Sciences Suzhou Institute of Nano-tech and Nano-Bionics View all articles by this author Shibao Sun Henan University of Science and Technology View all articles by this author Metrics & Citations Metrics Article Usage 202 views 95 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xingjia Wei, Pengcheng Zhao, Yatong Wang, et al. not-yet-known not-yet-known not-yet-known unknown SLSVD: A Swarm Learning with Singular Value Decomposition for Industrial Digital Twins. Authorea . 27 March 2025. DOI: https://doi.org/10.22541/au.174305177.71186360/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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