"Guest Editorial Introduction to the Special Section on Collaborative Machine Learning for Next-Generation Intelligent Applications" by Wei Cai

Article

Guest Editorial Introduction to the Special Section on Collaborative Machine Learning for Next-Generation Intelligent Applications

IEEE Transactions on Network Science and Engineering

Wei Cai, The Chinese University of Hong Kong, Shenzhen
Zehui Xiong, Singapore University of Technology and Design
Jiawen Kang, Guangdong University of Technology
Carla Fabiana Chiasserini, Politecnico di Torino
Ekram Hossain, University of Manitoba
Mohsen Guizani, Mohamed Bin Zayed University of Artificial Intelligence

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Article

Abstract

The papers in this special issue focus on collaborative machine learning for next generation intelligent applications. As a distributed learning technology, collaborative machine learning (CML) has been recently introduced to collaboratively train a model among multiple networking agents by using on-device computation. By integrating the high-potential CML with advanced emerging technologies, next-generation intelligent applications will provide more efficient, intelligent, and secure services, which may dramatically enhance the life experience of humans and revolutionize modern business. However, there are still many open challenges in this area. CML needs significant research efforts on theories, algorithms, architecture, and experiences of system deployment and maintenance. This special issue aims to offer a platform for researchers from both academia and industry to publish recent research findings and to discuss opportunities, challenges, and solutions related to collaborative machine learning.

DOI

10.1109/TNSE.2022.3195370

Publication Date

9-9-2022

Keywords

Computation theory

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Citation Information

W. Cai, Z. Xiong, J. Kang, C. F. Chiasserini, E. Hossain and M. Guizani, "Guest Editorial Introduction to the Special Section on Collaborative Machine Learning for Next-Generation Intelligent Applications," in IEEE Transactions on Network Science and Engineering, vol. 9, no. 5, pp. 3095-3098, 1 Sept.-Oct. 2022, doi: 10.1109/TNSE.2022.3195370.