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.
Guest Editorial Introduction to the Special Section on Collaborative Machine Learning for Next-Generation Intelligent Applications / Cai, Wei; Xiong, Zehui; Kang, Jiawen; Chiasserini, Carla Fabiana; Hossain, Ekram; Guizani, Mohsen. - In: IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING. - ISSN 2327-4697. - ELETTRONICO. - 9:5(2022), pp. 3095-3098. [10.1109/TNSE.2022.3195370]
Guest Editorial Introduction to the Special Section on Collaborative Machine Learning for Next-Generation Intelligent Applications
Carla Fabiana Chiasserini;
2022
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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2971222