Distributed Machine Learning, Optimization and Applications

Recent advances in machine learning, information processing, multi-agent control, computational intelligence and networking have resulted in increasingly big data and distributed spatial data storage, which lead to new demands for machine learning to design more complex models and learning algorithms. In order to run algorithms with big data, the distributed machine leaning models and optimization algorithms are often required in engineering applications. This has inspired a lot of efficient learning algorithms and systems in artificial intelligence for data parallelism and model parallelism, including reinforcement learning, federated learning, deep learning, multi-task learning, and multi-agent systems. In addition, there is a surge of research activities devoted to the research of theories and applications of data driven and robust learning models on machine learning. With the development of distributed machine learning and optimization, the learning approaches have demonstrated remarkable performance across a range of applications, such as face and speech recognition, computer vision, image classification, computer game, multi-robot control, real-time resource allocation, etc.

This special issue aims to present the latest theoretical and technical advancements in the broad area of distributed machine learning and optimization for data, model and system analysis. The list of possible topics includes, but is not limited to:

• Distributed algorithms in machine learning
• Multi-agent reinforcement learning
• Multi-task optimization and learning
• Federated optimization and learning
• Distributed optimization and real-time computing
• Applications of distributed machine leaning and optimization

2. Important Dates

Submission Deadline: March 31, 2021

First Review Decision: May 31, 2021

Revisions Due: July 15, 2021

Final Manuscript: September 30, 2021

Expected publication date: November 1, 2021

3. Guest Editors

Dr. Qingshan Liu, Southeast University, Nanjing, Jiangsu, China, qsliu@seu.edu.cn

Dr. Zhigang Zeng, Huazhong University of Science and Technology, Wuhan, Hubei, China, zgzeng@hust.edu.cn

Dr. Yaochu Jin, University of Surrey, Guildford, Surrey, United Kingdom, yaochu.jin@surrey.ac.uk

4. Submission Instructions

Authors should prepare their manuscripts according to the “Instructions for Authors” guidelines of “Neurocomputing” outlined at the journal website https://www.elsevier.com/journals/neurocomputing/0925-2312/guide-for-authors. All papers will be peer-reviewed following a regular reviewing procedure. Each submission should clearly demonstrate evidence of benefits to society or large communities. Originality and impact on society, in combination with a media-related focus and innovative technical aspects of the proposed solutions will be the major evaluation criteria.