IEEE TCCN Special Section Editorial: Convergence of Collaborative Distributed Machine Learning and Edge Computing Toward Intelligent Networking

We are delighted to introduce the readers to this special Section of the IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING (TCCN), which aims at soliciting the original research and practical contributions on novel architectures, applications, and technologies of the convergence of Collab...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on cognitive communications and networking Vol. 7; no. 4; pp. 1016 - 1020
Main Authors: Niyato, Dusit, Huang, Kaibin, Bennis, Mehdi, Pan, Miao, Xiong, Zehui, Bao Le, Long, Kim, Dong in, Wang, Li-Chun
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Alliances
Collaboration
Communications systems
Convergence
Edge computing
Machine learning
ISSN:2332-7731, 2332-7731
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We are delighted to introduce the readers to this special Section of the IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING (TCCN), which aims at soliciting the original research and practical contributions on novel architectures, applications, and technologies of the convergence of Collaborative Distributed Machine Learning (CD-ML) and edge computing towards the realization of "edge intelligence" and/or "intelligent edge." The objective of this special Section is to introduce recent advances in convergence of CD-ML and edge computing, and its applications to tackle both fundamental and practical challenges in intelligent communication systems. We have received a total number of 26 submissions, and after a rigorous peer-review process, 9 articles have been selected for publication, which are briefly discussed as follows.
Bibliography:SourceType-Scholarly Journals-1
content type line 14
ObjectType-Editorial-2
ObjectType-Commentary-1
ISSN:2332-7731
2332-7731
DOI:10.1109/TCCN.2021.3127563