[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116878-en":3,"doc-seo-116878-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116878,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","A Survey From Distributed Machine Learning to Distributed Deep Learning","Artificial intelligence achieves strong results in complex tasks through advances in machine learning algorithms and hardware acceleration, but higher accuracy and harder problem-solving require training on massive datasets. Processing such data can be time-consuming and computationally demanding, motivating distributed machine learning by splitting data and algorithms across multiple machines. This article reviews state-of-the-art distributed machine learning methods, organizing them into classification/clustering, deep learning, and deep reinforcement learning, and emphasizes distributed deep learning while identifying key limitations for future research.","A Survey From Distributed Machine Learning to Distributed Deep Learning  \nMohammad Dehghani 1*, Zahra Yazdanparast2  \n*Correspondence: [dehghani.mohammad@ut.ac.ir](dehghani.mohammad@ut.ac.ir)  \n1 School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran  \n2 School of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran  \nAbstract  \nArtificial intelligence has achieved significant success in handling complex tasks in recent years. This success is due to advances in machine learning algorithms and hardware acceleration. In order to obtain more accurate results and solve more complex problems, algorithms must be trained with more data. This huge amount of data could be time-consuming to process and require a great deal of computation. This solution could be achieved by distributing the data and algorithm across several machines, which is known as distributed machine learning. There has been considerable effort put into distributed machine learning algorithms, and different methods have been proposed so far. In this article, we present a comprehensive summary of the current state-of-the-art in the field through the review of these algorithms. We divide this algorithms in classification and clustering (traditional machine learning), deep learning and deep reinforcement learning groups. Distributed deep learning has gained more attention in recent years and most of studies worked on this algorithms. As a result, most of the articles we discussed here belong to this category. Based on our investigation of algorithms, we highlight limitations that should be addressed in future research.  \nKeywords: Artificial intelligence, Machine learning, Distributed machine learning, Ditributed deep learning, Ditributed reinforcement learning, Data-parallelism, Model-parallelism  \n1. Introduction  \nArtificial intelligence (AI) has developed rapidly in recent years to make applications more intelligent [1] . AI uses knowledge and analyzes it to simulate human behaviors and train computers to learn, make judgments, and make decisions similarly to humans [2, 3] . AI involves the development of techniques and algorithms that are capable of thinking, acting, and implementing tasks using protocols that are otherwise beyond human comprehension [4] .  \nMachine learning (ML) is a subset of artificial intelligence that learns from historical data, without being explicitly programmed [5] . As a result of Industry 4.0, the digital world has been provided with a large amount of data in many different areas, such as cybersecurity, business, health, and the Internet of Things (IOT). This wealth of data is analyzed by Machine Learning, one of the most popular technologies in Industry 4.0, in order to develop smarter and more automated systems [6] .  \nA variety of ML algorithms can be used to analyze data and build data-driven systems, including classification, clustering, regression, association rule mining, and reinforcement learning [7, 8] . Moreover, deep learning is a branch of machine learning that uses artificial neural networks to intelligently analyze large amounts of data [9, 10] . ML algorithms have been widely used in many applications domains, such as computer vision, natural language processing (NLP), recommender systems, and user behavior analytics, across many industries, including education [11], healthcare [12], marketing [13], transportation [14], energy [15], combustion science [16], construction, and manufacturing [17] .  \nA machine learning solution is influenced by the characteristics of the data and the performance of the learning algorithms [6] . Traditionally, a bottleneck in developing more intelligent systems was data availability which is no longer the case. The problem, however, is that learning algorithms are unable to utilize all the data within a reasonable period of time for learning. Creating an effective ML model is generally a complex and time-consuming process that involves selec","cbCaiih92dEyqYD1","https://ap.wps.com/l/cbCaiih92dEyqYD1","pdf",445609,1,21,"English","en",105,"# Introduction\n## Artificial Intelligence and Machine Learning\n## Industry 4.0 Data and Application Domains\n## Motivation for Distributed Machine Learning\n## Focus on Distributed Deep Learning\n## Paper Organization","[{\"question\":\"Why does the document motivate distributed machine learning?\",\"answer\":\"Training accurate models needs large volumes of data, but processing and computation on a single machine can be too slow. Distributed approaches split data and training across multiple machines to improve scalability and handle naturally distributed datasets.\"},{\"question\":\"How does the document categorize distributed machine learning algorithms?\",\"answer\":\"It groups work into traditional machine learning tasks (classification and clustering) and into deep learning plus deep reinforcement learning, with distributed deep learning receiving particular emphasis.\"},{\"question\":\"What future research directions does the document highlight?\",\"answer\":\"Based on the review, it highlights limitations of current algorithms and points out issues that should be addressed in subsequent research, especially in the context of distributed deep learning.\"}]","A Survey From Distributed Machine Learning to Distributed Deep Learning | PDF",1785672192,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-survey-from-distributed-machine-learning-to-distributed-deep-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-survey-from-distributed-machine-learning-to-distributed-deep-learning/116878/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does the document motivate distributed machine learning?","Question",{"text":76,"@type":77},"Training accurate models needs large volumes of data, but processing and computation on a single machine can be too slow. Distributed approaches split data and training across multiple machines to improve scalability and handle naturally distributed datasets.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the document categorize distributed machine learning algorithms?",{"text":81,"@type":77},"It groups work into traditional machine learning tasks (classification and clustering) and into deep learning plus deep reinforcement learning, with distributed deep learning receiving particular emphasis.",{"name":83,"@type":74,"acceptedAnswer":84},"What future research directions does the document highlight?",{"text":85,"@type":77},"Based on the review, it highlights limitations of current algorithms and points out issues that should be addressed in subsequent research, especially in the context of distributed deep learning.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]