Wireless Networks - Machine Learning Powered Distributed Computing in Wireless Networks
Overview:
With the development of 5G communications, edge computing, and artificial intelligence (AI), we can process and mine the value of big data by using distributed computing technology. It has applied to multiple fields such as intelligent driving, healthcare monitoring, recommendation systems, scientific computing, and Smart Ocean. However, the scale of the AI model is getting larger and larger, and the parameters are increasing exponentially, such as GPT-3, Huawei Pangu model, Enlightenment, etc. At the same time, the datasets are also getting larger and larger, such as ImageNet-1K, Google Open Images and Tencent ML-Images, etc. To this point, there are research challenges, such as how to conduct distributed high-performance training and inference? How to protect data privacy when training an AI model? How to store and read AI training data efficiently, and so on.
Therefore, distributed training, inference model and framework, data privacy, data processing and storage, and distributed algorithms for AI should be investigated in depth. These are in the early research stage for the next generation of large-scale AI. This special issue focuses on advances in Distributed ML in Wireless Networks. Researchers from academic fields and industries worldwide are encouraged to submit high-quality unpublished original research articles as well as review articles in broad areas relevant to theories, technologies, and emerging applications.
Topics
Topics of interest include, but are not limited to, the following scope:
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Important Dates
Manuscript submission deadline: December 25, 2023
Notification of acceptance: January 31, 2024
Submission of final revised paper: March 31, 2024
Publication of special issue (tentative): December 2024
Submission Procedure
Authors should follow the WINET Journal manuscript format described at the journal site. Manuscripts should be submitted online through http://www.editorialmanager.com/wine/ (this opens in a new tab).
A copy of the manuscript should also be emailed to the Guest Editors at the following email address gaohonghao@shu.edu.cn
Guest Editors:
Prof. Honghao Gao, Shanghai University, China, gaohonghao@shu.edu.cn
Prof. Xinheng Wang Xi'an Jiaotong Liverpool University, China, xinheng.wang@xjtlu.edu.cn
Prof. Tun Lu, Fudan University, China, lutun@fudan.edu.cn