Machine Learning Powered Distributed Computing in Wireless Networks

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 Researchers from academic fields and industries worldwide are encouraged to submit high-quality unpublished original research articles as well as review articles in broad areas relevant to theories, technologies, and emerging applications.