Scalable and efficient distributed learning is one of the main
driving forces behind the recent rapid advancement of machine
learning and artificial intelligence. One prominent feature of this
development is that recent progress has been made by researchers in
two communities: (1) the system community such as database, data
management, and distributed systems, and (2) the machine learning
and mathematical optimization community. The interaction and
knowledge sharing between these two communities has led to the
rapid development of new distributed learning systems and theory.
This monograph provides a brief introduction to three distributed
learning techniques that have recently been developed: lossy
communication compression, asynchronous communication, and
decentralized communication. These have significant impact on the
work in both the system and machine learning and mathematical
optimization communities but to fully realize the potential, it is
essential they understand the whole picture. This monograph
provides the bridge between the two communities. The simplified
introduction to the essential aspects of each community enables
researchers to gain insights into the factors influencing both.The
monograph provides students and researchers the groundwork for
developing faster and better research results in this dynamic area
of research.
General
Imprint: |
Now Publishers Inc
|
Country of origin: |
United States |
Series: |
Foundations and Trends (R) in Databases |
Release date: |
June 2020 |
First published: |
2020 |
Authors: |
Ji Liu
• Ce Zhang
|
Dimensions: |
234 x 156mm (L x W) |
Format: |
Paperback
|
Pages: |
108 |
ISBN-13: |
978-1-68083-700-1 |
Categories: |
Books >
Computing & IT >
Applications of computing >
Databases >
General
|
LSN: |
1-68083-700-1 |
Barcode: |
9781680837001 |
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