DSA Thrust Seminar | TinyML Systems for Edge Intelligence
Supporting the below United Nations Sustainable Development Goals:支持以下聯合國可持續發展目標：支持以下联合国可持续发展目标：
This talk will focus on the field of Edge Learning. Specifically, learning paradigms, fundamental theories, and enabling technologies for Edge Learning consist the main components of this tutorial. We will first explain the background and motivation for ML running at the network edge. Then, we will review the challenge issues existing in Edge Learning. Furthermore, we will provide an overview of the overarching architectures, frameworks, and emerging key technologies for learning performance, security, privacy, and incentive issues toward training/inference at the network edge. Finally, we will discuss future research opportunities on Edge Learning.
Song Guo is a Full Professor at the Department of Computing, The Hong Kong Polytechnic University. He also holds a Changjiang Chair Professorship awarded by the Ministry of Education of China. Prof. Guo is a Fellow of the Canadian Academy of Engineering, Fellow of the IEEE, and ACM Distinguished Member. Prof. Guo’s research interests are mainly in edge AI, big data and machine learning, mobile computing, and distributed systems. He published many papers in top venues with wide impact in these areas and was recognized as a Highly Cited Researcher (Clarivate Web of Science). Prof. Guo has served as an IEEE ComSoc Distinguished Lecturer, Director of ComSoc Membership Services, member of IEEE ComSoc Board of Governors, and the current Chair of IEEE Communications Society Space and Satellite Communications Technical Committee. He also serves for IEEE Computer Society on Fellow Evaluation Committee, the inaugural Editor-in-Chief of IEEE Open Journal of the Computer Society, and on editorial board of a number of prestigious IEEE Transactions and Journals. He has served as chair of organizing and technical committees of numerous international conferences.