Workshop 15: Distributed Learning for Smart and Practical IoT
Title: Need for Intelligence: Learning in the Internet of Things

Keywords: Federated Learning, Swarm Learning, Blockchain, Multi-agent Reinforcement Learning, IoT

Summary: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. Peng Lin |
Nanjing University of Information Science and Technology
Peng Lin received the Ph.D degrees in communication and information systems from Northeastern University, Shenyang, China in 2021. From 2019 to 2020, he visited the University of British Columbia, Vancouver, Canada and Carleton University, Ottawa, Canada as a visiting scholar. He was introduced to Nanjing University of Information Science and Technology as a Longshan Scholar in 2021. He has published many top international journals and conferences including IEEE WCM, TWC, TII, TVT, IoTJ, ICCC, etc. He served as the reviewers for many IEEE journals and conferences. His current research interests include mobile edge computing, edge caching, and machine learning in wireless networks.