Handbook on Federated Learning: Advances, Applications and Opportunities
暫譯: 聯邦學習手冊：進展、應用與機會

Krishnan, Saravanan, Anand, A. Jose, Srinivasan, R.

Handbook on Federated Learning: Advances, Applications and Opportunities
  • 出版商: CRC
  • 出版日期: 2023-12-15
  • 售價: $5,500
  • 貴賓價: 9.5 折 $5,225
  • 語言: 英文
  • 頁數: 356
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 103247162X
  • ISBN-13: 9781032471624
  • 相關分類: 人工智慧、大數據 Big-data、Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.

商品描述(中文翻譯)

行動裝置、可穿戴裝置和自駕電話只是現代分散式網路的一些例子，這些網路每天產生大量資訊。由於這些裝置的計算能力不斷增強，以及對私人資訊傳輸的擔憂，將部分數據在本地處理變得越來越重要，這意味著將學習方法和計算移至裝置的邊緣。聯邦學習（Federated Learning, FL）在這種情況下發展成為一種教育模型。聯邦學習是一種去中心化的機器學習（Machine Learning, ML）專家形式。它在隱私、大規模機器教育和分配等領域至關重要。它也基於當前的資訊通信技術（ICT）和新硬體技術，是下一代人工智慧（Artificial Intelligence, AI）。在聯邦學習中，中央機器學習模型是基於傳統機器學習中可用的所有數據在集中環境中建立的。當預測可以由中央伺服器提供時，它運作良好。用戶在行動計算中需要快速響應，但模型處理發生在伺服器端，這樣會花費過長的時間。模型可以放置在最終用戶裝置中，但持續學習是一個需要克服的挑戰，因為模型是基於完整數據集編程的，而最終用戶裝置無法訪問整個數據包。傳統機器學習的另一個挑戰是用戶數據在中央位置聚合，這違反了當地的隱私政策法律，並使數據更容易受到數據違規的威脅。本書提供了聯邦學習在各個方面的綜合方法。

作者簡介

Saravanan Krishnan is working as Associate Professor at the Department of Computer Science & Engineering, College of Engineering, Guindy, Anna University, Tirunelveli, India. He has published papers in 14 international conferences and 30 reputed journals. He has also written 16 book chapters and nine books with reputed publishers. He is an active researcher and academician. Also, he is reviewer for many reputed journals published by Elsevier, IEEE etc.

A. Jose Anand is working as Professor at the Department of Electronics and Communication Engineering, KCG College of Technology, Chennai, India. He has one year of industrial experience and twenty-four years of teaching experience. He has presented several papers at conferences. He has published several papers in reputed journals. He has also published books for polytechnic & engineering subjects. He is a Member of CSI, IEI, IET, IETE, ISTE, INS, QCFI and EWB. His current research interest is in Wireless Sensor Networks, Embedded Systems, IoT, Machine Learning and Image Processing, etc.

R. Srinivasan is working as Professor at the Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India having vast teaching experience. He received a Ph.D. in Computer Science and Engineering from Vel Tech University. His research interest spans across Computer Networking, Wireless Sensor Networks and Internet of Things (IoT). Much of his work has been on improvising the understanding, design and the performance of networked computer systems and performance evaluation. He is a recognised supervisor at Vel Tech University guiding 8 research scholars. He has published over 25 papers in reputed journals and conferences. He had delivered technical sessions to various reputed institutes. He has been a reviewer member for many conferences and has served as technical committee member. He is also a member in many professional societies and a member in IEEE. He has published several reputed articles. He is presently Editor in Chief for Wireless Networks, Peer-to-Peer Networking and Applications- Springer Series.

R. Kavitha received a master's in software engineering from College of Engineering, Anna University, India and Ph. D in Computer Science and Engineering from Vel Tech, Chennai, India. Her research areas are Machine Learning, Image Processing and Software Engineering. She worked as Professor at Vel Tech, Chennai with 15 years of teaching experience. She had guided projects of many UG and PG students. She is a recognised supervisor at Vel Tech University guiding 8 research scholars. She has published over 35 papers in reputed journals. She is an active member of IEEE and IEEE WIE and has been a part of events in association with professional societies. She had delivered technical sessions to various reputed institutes. She has been a reviewer member for many conferences and has served as technical committee member.

S. Suresh was a Professor of Cloud Big Data and Analytics, Faculty of Computer Science and Engineering at P.A. College of Engineering and Technology, India. He undertook extensive research on Big Data & Analytics, Internet of Things and Machine Learning. He wrote more than 30 scientific papers some of which have been published in well-known journals from Elsevier, Springer, etc. and presented at important conferences. In his lifetime, he had received various best paper and best speaker awards. Suresh authored 6 books and numerous book chapters. He fetched research and events grants from various Indian agencies. His research is summarized at Google Scholar Citation. He also regularly tutors, advises and provides consulting support to regional firms with respect to their Cloud Big Data Analytics, IoT, Machine Learning and Mobile Application Development.

作者簡介(中文翻譯)

**Saravanan Krishnan** 目前擔任印度提魯內維利安娜大學工程學院計算機科學與工程系的副教授。他在14個國際會議和30本知名期刊上發表了論文，並為知名出版社撰寫了16個書章和九本書籍。他是一位活躍的研究者和學者，同時也是許多由Elsevier、IEEE等出版的知名期刊的審稿人。

**A. Jose Anand** 目前擔任印度金奈KCG科技學院電子與通信工程系的教授。他擁有一年的工業經驗和二十四年的教學經驗，並在會議上發表了多篇論文，並在知名期刊上發表了多篇論文。他還為專科和工程科目出版了書籍。他是CSI、IEI、IET、IETE、ISTE、INS、QCFI和EWB的成員。他目前的研究興趣包括無線感測器網路、嵌入式系統、物聯網、機器學習和影像處理等。

**R. Srinivasan** 目前擔任印度金奈Vel Tech Rangarajan Dr. Sagunthala科學與技術研究所計算機科學與工程系的教授，擁有豐富的教學經驗。他在Vel Tech大學獲得計算機科學與工程的博士學位。他的研究興趣涵蓋計算機網路、無線感測器網路和物聯網（IoT）。他的許多工作集中在改善網路計算機系統的理解、設計和性能評估上。他是Vel Tech大學的認可指導教師，指導8名研究學者。他在知名期刊和會議上發表了超過25篇論文，並為多個知名機構提供技術講座。他曾擔任多個會議的審稿成員，並擔任技術委員會成員。他也是多個專業學會的成員，並且是IEEE的成員。他發表了多篇知名文章，目前擔任《無線網路》、《點對點網路與應用-施普林格系列》的主編。

**R. Kavitha** 於印度安娜大學工程學院獲得軟體工程碩士學位，並在Vel Tech金奈獲得計算機科學與工程的博士學位。她的研究領域包括機器學習、影像處理和軟體工程。她在Vel Tech金奈擔任教授，擁有15年的教學經驗，並指導了許多本科和研究生的專案。她是Vel Tech大學的認可指導教師，指導8名研究學者。她在知名期刊上發表了超過35篇論文。她是IEEE和IEEE WIE的活躍成員，並參與了與專業學會相關的活動。她曾為多個知名機構提供技術講座，並擔任多個會議的審稿成員和技術委員會成員。

**S. Suresh** 曾是印度P.A.工程與技術學院計算機科學與工程系的雲端大數據與分析教授。他在大數據與分析、物聯網和機器學習方面進行了廣泛的研究。他撰寫了超過30篇科學論文，其中一些已發表在知名期刊如Elsevier、Springer等，並在重要會議上發表。在他的職業生涯中，他獲得了多個最佳論文和最佳演講者獎項。Suresh著有6本書和多篇書章。他從各種印度機構獲得了研究和活動資助。他的研究在Google Scholar Citation上有總結。他還定期為地區公司提供雲端大數據分析、物聯網、機器學習和行動應用開發方面的輔導、建議和諮詢支持。

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