Compression and Transmission of Big AI Model Based on Deep Learning: Compression and Transmission of Big AI Model Based on Deep Learning | EAI Endorsed Transactions on Scalable Information Systems

<!DOCTYPE html> <html lang="en-US" xml:lang="en-US"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Compression and Transmission of Big AI Model Based on Deep Learning: Compression and Transmission of Big AI Model Based on Deep Learning | EAI Endorsed Transactions on Scalable Information Systems </title> <link rel="icon" href="https://publications.eai.eu/public/journals/8/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.3.0.18"> <meta property="og:site_name" content="EAI Endorsed Transactions on Scalable Information Systems"/> <meta property="og:type" content="article"/> <meta property="og:title" content="Compression and Transmission of Big AI Model Based on Deep Learning: Compression and Transmission of Big AI Model Based on Deep Learning"/> <meta name="description" property="og:description" content=" In recent years, big AI models have demonstrated remarkable performance in various artificial intelligence (AI) tasks. However, their widespread use has introduced significant challenges in terms of model transmission and training. This paper addresses these challenges by proposing a solution that involves the compression and transmission of large models using deep learning techniques, thereby ensuring the efficiency of model training. To achieve this objective, we leverage deep convolutional networks to design a novel approach for compressing and transmitting large models. Specifically, deep convolutional networks are employed for model compression, providing an effective means to reduce the size of large models without compromising their representational capacity. The proposed framework also includes carefully devised encoding and decoding strategies to guarantee the restoration of model integrity after transmission. Furthermore, a tailored loss function is designed for model training, facilitating the optimization of both the transmission and training performance within the system. Through experimental evaluation, we demonstrate the efficacy of the proposed approach in addressing the challenges associated with large model transmission and training. The results showcase the successful compression and subsequent accurate reconstruction of large models, while maintaining their performance across various AI tasks. This work contributes to the ongoing research in enhancing the practicality and efficiency of deploying large models in real-world AI applications. "/> <meta property="og:url" content="https://publications.eai.eu/index.php/sis/article/view/3803"/> <meta name="og:locale" content="en_US"/> <meta name="image" property="og:image" content=""/> <meta name="article:published_time" content="2023-12-11"/> <meta name="article:tag" content="Big AI model"/> <meta name="article:tag" content="compression and transmission"/> <meta name="article:tag" content="deep learning"/> <meta name="article:tag" content="convolutional networks"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Zhengping Lin"/> <meta name="DC.Creator.PersonalName" content="Yuzhong Zhou"/> <meta name="DC.Creator.PersonalName" content="Yuliang Yang"/> <meta name="DC.Creator.PersonalName" content="Jiahao Shi"/> <meta name="DC.Creator.PersonalName" content="Jie Lin"/> <meta name="DC.Date.created" scheme="ISO8601" content="2023-12-11"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2023-08-28"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-01-11"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-01-11"/> <meta name="DC.Description" xml:lang="en" content="In recent years, big AI models have demonstrated remarkable performance in various artificial intelligence (AI) tasks. However, their widespread use has introduced significant challenges in terms of model transmission and training. This paper addresses these challenges by proposing a solution that involves the compression and transmission of large models using deep learning techniques, thereby ensuring the efficiency of model training. To achieve this objective, we leverage deep convolutional networks to design a novel approach for compressing and transmitting large models. Specifically, deep convolutional networks are employed for model compression, providing an effective means to reduce the size of large models without compromising their representational capacity. The proposed framework also includes carefully devised encoding and decoding strategies to guarantee the restoration of model integrity after transmission. Furthermore, a tailored loss function is designed for model training, facilitating the optimization of both the transmission and training performance within the system. Through experimental evaluation, we demonstrate the efficacy of the proposed approach in addressing the challenges associated with large model transmission and training. The results showcase the successful compression and subsequent accurate reconstruction of large models, while maintaining their performance across various AI tasks. This work contributes to the ongoing research in enhancing the practicality and efficiency of deploying large models in real-world AI applications."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="3803"/> <meta name="DC.Identifier.DOI" content="10.4108/eetsis.3803"/> <meta name="DC.Identifier.URI" content="https://publications.eai.eu/index.php/sis/article/view/3803"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2023 Zhengping Lin, Yuzhong Zhou, Yuliang Yang, Jiahao Shi, Jie Lin"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by-nc-sa/4.0"/> <meta name="DC.Source" content="EAI Endorsed Transactions on Scalable Information Systems"/> <meta name="DC.Source.ISSN" content="2032-9407"/> <meta name="DC.Source.Issue" content="2"/> <meta name="DC.Source.Volume" content="11"/> <meta name="DC.Source.URI" content="https://publications.eai.eu/index.php/sis"/> <meta name="DC.Subject" xml:lang="en" content="convolutional networks"/> <meta name="DC.Title" content="Compression and Transmission of Big AI Model Based on Deep Learning"/> <meta name="DC.Type" content="Text.Serial.Journal"/> <meta name="DC.Type.articleType" content="Research articles"/> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="EAI Endorsed Transactions on Scalable Information Systems"/> <meta name="citation_journal_abbrev" content="EAI Endorsed Scal Inf Syst"/> <meta name="citation_issn" content="2032-9407"/> <meta name="citation_author" content="Zhengping Lin"/> <meta name="citation_author_institution" content="China Southern Power Grid (China) "/> <meta name="citation_author" content="Yuzhong Zhou"/> <meta name="citation_author_institution" content="China Southern Power Grid (China) "/> <meta name="citation_author" content="Yuliang Yang"/> <meta name="citation_author_institution" content="China Southern Power Grid (China) "/> <meta name="citation_author" content="Jiahao Shi"/> <meta name="citation_author_institution" content="China Southern Power Grid (China) "/> <meta name="citation_author" content="Jie Lin"/> <meta name="citation_author_institution" content="China Southern Power Grid (China) "/> <meta name="citation_title" content="Compression and Transmission of Big AI Model Based on Deep Learning: Compression and Transmission of Big AI Model Based on Deep Learning"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2024"/> <meta name="citation_volume" content="11"/> <meta name="citation_issue" content="2"/> <meta name="citation_doi" content="10.4108/eetsis.3803"/> <meta name="citation_abstract_html_url" content="https://publications.eai.eu/index.php/sis/article/view/3803"/> <meta name="citation_keywords" xml:lang="en" content="Big AI model"/> <meta name="citation_keywords" xml:lang="en" content="compression and transmission"/> <meta name="citation_keywords" xml:lang="en" content="deep learning"/> <meta name="citation_keywords" xml:lang="en" content="convolutional networks"/> <meta name="citation_pdf_url" content="https://publications.eai.eu/index.php/sis/article/download/3803/2746"/> <meta name="citation_reference" content="A. 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1.26,0.17 2.38,0.5 3.35,0.99 0.97,0.49 1.78,1.19 2.41,2.11 0.63,0.92 0.95,2.15 0.95,3.7 0,1.65 -0.36,2.94 -1.08,3.86 z" id="path80" inkscape:connector-curvature="0" style="fill:#202826" /> </g> </g> </switch> </svg> </a> </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.4108/eetsis.3803"> https://doi.org/10.4108/eetsis.3803 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> Big AI model, compression and transmission, deep learning, convolutional networks </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>In recent years, big AI models have demonstrated remarkable performance in various artificial intelligence (AI) tasks. However, their widespread use has introduced significant challenges in terms of model transmission and training. This paper addresses these challenges by proposing a solution that involves the compression and transmission of large models using deep learning techniques, thereby ensuring the efficiency of model training. To achieve this objective, we leverage deep convolutional networks to design a novel approach for compressing and transmitting large models. Specifically, deep convolutional networks are employed for model compression, providing an effective means to reduce the size of large models without compromising their representational capacity. The proposed framework also includes carefully devised encoding and decoding strategies to guarantee the restoration of model integrity after transmission. Furthermore, a tailored loss function is designed for model training, facilitating the optimization of both the transmission and training performance within the system. Through experimental evaluation, we demonstrate the efficacy of the proposed approach in addressing the challenges associated with large model transmission and training. The results showcase the successful compression and subsequent accurate reconstruction of large models, while maintaining their performance across various AI tasks. This work contributes to the ongoing research in enhancing the practicality and efficiency of deploying large models in real-world AI applications.</p> </section>  <a href="https://plu.mx/plum/a/?doi=10.4108/eetsis.3803" class="plumx-summary" data-hide-when-empty="true" data-orientation="vertical" ></a>  <section class="item references"> <h2 class="label"> References </h2> <div class="value"> <p>A. E. Haddad and L. Najafizadeh, “The discriminative discrete basis problem: Definitions, algorithms, benchmarking, and application to brain’s functional dynamics,” IEEE Trans. Signal Process., vol. 71, pp. 1–16, 2023. </p> <p>R. Gabrys, S. Pattabiraman, and O. 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Bao, “Federated learning with adaptive communication compression under dynamic bandwidth and unreliable networks,” Information Sciences, vol. 540, pp. 242–262, 2020. </p> </div> </section> </div> <div class="entry_details"> <div class="item galleys"> <h2 class="pkp_screen_reader"> Downloads </h2> <ul class="value galleys_links"> <li> <a class="obj_galley_link pdf" href="https://publications.eai.eu/index.php/sis/article/view/3803/2746"> PDF </a> </li> </ul> </div> <div class="item published"> <section class="sub_item"> <h2 class="label"> Published </h2> <div class="value"> <span>11-12-2023</span> </div> </section> </div> <div class="item citation"> <section class="sub_item citation_display"> <h2 class="label"> How to Cite </h2> <div class="value"> <div id="citationOutput" role="region" aria-live="polite"> <div class="csl-bib-body"> <div class="csl-entry"><div class="csl-left-margin">1.</div><div class="csl-right-inline">Lin Z, Zhou Y, Yang Y, Shi J, Lin J. Compression and Transmission of Big AI Model Based on Deep Learning: Compression and Transmission of Big AI Model Based on Deep Learning. EAI Endorsed Scal Inf Syst [Internet]. 2023 Dec. 11 [cited 2025 Apr. 10];11(2). 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Compression and Transmission of Big AI Model Based on Deep Learning: Compression and Transmission of Big AI Model Based on Deep Learning | EAI Endorsed Transactions on Scalable Information Systems