Internet of Things (IoT) and machine learning are two important techniques in most industrial, business, agricultural, and medical applications. On the one hand, IoT systems keep producing massive sensory data as the input of various services. On the other hand, machine learning has obtained great success in vision, graphics, natural language processing, gaming, and controlling. This workshop calls for works demonstrating the most recent progress and contributions to learning in IoT. In particular, this workshop will focus on the follows (1) In-network federated learning, which does not need a center for sensory data sharing, but trains the machine learning model in a distributed fashion within the IoT; (2) Swarm learning that unites edge computing, blockchain-based peer-to-peer networking, without the need for a central coordinator. (3) Multi-agent reinforcement learning schemes for control of charging and moving, or decision making of communication, resource allocation, task scheduling, etc. This workshop especially encourages applications of learning techniques that make battery charging, event detection, localization in IoTs practical.
Chair: Dr. Hejun Wu |
Sun Yat-sen University
Hejun Wu works as an associate professor at the School of Computer Science and Engineering. He is also with the School of Artificial Intelligence, Sun Yat-Sen University. His main research interests are Artificial Intelligent Internet of Things (AIoT) and Mobile Internet of Things (MIoT), clusters of autonomous mobile robots, and distributed parallel perception. He was the principal investigator of projects granted from the General Program of the National Natural Science Foundation of China. Besides, he participated in the Major Research Plan of the National Natural Science Foundation of China and the key project of the National Programs for Science and Technology Development. Moreover, he has published more than 40 papers on top international conferences and journals in recent years including IEEE IoT, TPDS, TWC, TKDE, TCSVT, ACM TWEB, INFOCOM, etc. He won the IEEE WCNC Best Paper Award and ISSNIP Best Paper Award.