Distributed Intelligence: From An Algorithmic Perspective

August 3, 2023 3:00 PM Singapore


Traditional AI algorithms entail resourceful and reliable computation infrastructure, whereas their efficacy cannot be ensured in many emerging distributed systems (e.g., sensor networks, vehicular networks, edge networks etc.). Deploying AI algorithms in these distributed systems entail many new concerns including fault tolerance, resource efficiency, privacy preservation etc. Furthermore, recent years have witnessed the development of AI algorithms, which provides a new way to improve the intellectuality of the distributed systems such that the efficacy of the systems can be guaranteed in application scenarios with uncertainty. In this talk, by following the above roadmap, I will highlight several research projects conducted by myself and my collaborators in the areas of distributed intelligence, mainly from an algorithmic perspective.

About the Speaker

Feng Li received his PhD degree in Computer Science from Nanyang Technological University, Singapore, in 2015. He got his MS and BS degrees from Shandong University in 2010 and Shandong Normal University in 2007, respectively. From 2014-2015, he was a research fellow in National University of Singapore, Singapore. After that, he joined School of Computer Science and Technology, Shandong University, China, where he is currently a professor. His research interests include distributed algorithms and systems, machine learning, and edge computing. He received several best paper awards from IEEE IPCCC 2020, CSoNet 2019 and IIKI 2019. He also received 2020 ACM China SIGAPP Rising Star Award due to his contributions in sensor networks, Internet of Things and their applications. He is guest editor for Computer Networks Journal and Computer Communications Journal. He also serves in the technical committees of IEEE ICC, IEEE GLOBECOM, IEEE IPCCC etc.

For more information about the ESD Seminar, please email esd_invite@sutd.edu.sg

Feng Li (Shandong University) - Distributed Intelligence: From An Algorithmic Perspective