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Distributed machine learning and computing = theory and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Distributed machine learning and computing/ edited by M. Hadi Amini.
其他題名:	theory and applications /
其他作者:	Amini, M. Hadi.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	x, 158 p. :ill. (chiefly col.), digital ;24 cm.
內容註:	Chapter 1. Distributed Machine Learning and Computing: An Overview -- Chapter 2. Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks -- Chapter 3. Heterogeneity Aware Distributed Machine Learning at the Wireless Edge for Health IoT Applications: An EEG Data Case Study -- Chapter 4. A Comprehensive Review of Artiﬁcial Intelligence and Machine Learning Methods for Modern Health-care Systems -- Chapter 5. Vertical Federated Learning: Principles, Applications, and Future Frontiers -- Chapter 6. Decentralization of Energy Systems with Blockchain: Bridging Top-down and Bottom-up Management of the Electricity Grid -- Chapter 7. Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-031-57567-9
ISBN:	9783031575679

Distributed machine learning and computing = theory and applications /
Distributed machine learning and computingtheory and applications /[electronic resource] :edited by M. Hadi Amini. - Cham :Springer International Publishing :2024. - x, 158 p. :ill. (chiefly col.), digital ;24 cm. - Big and integrated artificial intelligence,v. 22662-4141 ;. - Big and integrated artificial intelligence ;v. 2..

Chapter 1. Distributed Machine Learning and Computing: An Overview -- Chapter 2. Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks -- Chapter 3. Heterogeneity Aware Distributed Machine Learning at the Wireless Edge for Health IoT Applications: An EEG Data Case Study -- Chapter 4. A Comprehensive Review of Artiﬁcial Intelligence and Machine Learning Methods for Modern Health-care Systems -- Chapter 5. Vertical Federated Learning: Principles, Applications, and Future Frontiers -- Chapter 6. Decentralization of Energy Systems with Blockchain: Bridging Top-down and Bottom-up Management of the Electricity Grid -- Chapter 7. Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization.

This book focuses on a wide range of distributed machine learning and computing algorithms and their applications in healthcare and engineering systems. The contributors explore how these techniques can be applied to different real-world problems. It is suitable for students and researchers interested in conducting research in multidisciplinary areas that rely on distributed machine learning and computing techniques. Specifies the value of efficient theoretical methods in dealing with large-scale decision-making problems; Provides an investigation of distributed machine learning and optimization algorithms for large-scale networks; Includes basics and mathematical foundations needed to analyze and address the interdependent complex networks.

ISBN: 9783031575679

Standard No.: 10.1007/978-3-031-57567-9doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .D57 2024

Dewey Class. No.: 006.31

Distributed machine learning and computing = theory and applications /
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505 0 $a Chapter 1. Distributed Machine Learning and Computing: An Overview -- Chapter 2. Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks -- Chapter 3. Heterogeneity Aware Distributed Machine Learning at the Wireless Edge for Health IoT Applications: An EEG Data Case Study -- Chapter 4. A Comprehensive Review of Artiﬁcial Intelligence and Machine Learning Methods for Modern Health-care Systems -- Chapter 5. Vertical Federated Learning: Principles, Applications, and Future Frontiers -- Chapter 6. Decentralization of Energy Systems with Blockchain: Bridging Top-down and Bottom-up Management of the Electricity Grid -- Chapter 7. Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization.
520 $a This book focuses on a wide range of distributed machine learning and computing algorithms and their applications in healthcare and engineering systems. The contributors explore how these techniques can be applied to different real-world problems. It is suitable for students and researchers interested in conducting research in multidisciplinary areas that rely on distributed machine learning and computing techniques. Specifies the value of efficient theoretical methods in dealing with large-scale decision-making problems; Provides an investigation of distributed machine learning and optimization algorithms for large-scale networks; Includes basics and mathematical foundations needed to analyze and address the interdependent complex networks.
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