國立虎尾科技大學 |

Edge learning for distributed big data analytics : = theory, algorithms, and system design /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Edge learning for distributed big data analytics :/ Song Guo, Zhihao Qu.
Reminder of title:	theory, algorithms, and system design /
Author:	Song, Guo
other author:	Qu, Zhihao,
Description:	1 online resource (x, 217 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 21 Jan 2022).
Subject:	Edge computing. -
Online resource:	https://doi.org/10.1017/9781108955959
ISBN:	9781108955959 (ebook)

Edge learning for distributed big data analytics : = theory, algorithms, and system design /
Song, Guo(Computer scientist),

Edge learning for distributed big data analytics :theory, algorithms, and system design /Song Guo, Zhihao Qu. - 1 online resource (x, 217 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 21 Jan 2022).

Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.

ISBN: 9781108955959 (ebook)Subjects--Topical Terms:

1218269
Edge computing.

LC Class. No.: QA76.583 / .S66 2022

Dewey Class. No.: 005.758

Edge learning for distributed big data analytics : = theory, algorithms, and system design /
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520 $a Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.
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