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 Researchers from academic fields and industries worldwide are encouraged to submit high-quality unpublished original research articles as well as review articles in broad areas relevant to theories, technologies, and emerging applications.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:

Cloud and edge-based distributed training framework and algorithm in wireless networks Security and privacy issues for edge AI in wireless networks Federated Learning framework and algorithm in wireless networks Data processing algorithm for AI in wireless networks Distributed storage system, the algorithm for AI in wireless networks Distributed ML on programmability, representations of parallelisms, performance optimizations, and system architectures in wireless networks

Resource management and scheduling for AI in wireless networks Scaling and accelerating machine learning, deep learning, and computer vision applications in wireless data and machine learning techniques for distributed and parallel systems in wireless networks Fault tolerance, reliability, and availability in wireless networks Datacenter, high performance computing (HPC), cloud, serverless, and edge/IoT computing platforms in wireless networks

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