Abstract
The B5G/6G mobile telecommunication systems expect to realize Artificial Intelligence of Things (AIoT) applications with the help of edge intelligence/AI by the marriage of edge/fog computing and artificial intelligence (AI). Recently, Federated Learning (FL), a promising and privacy-preserving edge intelligence system, has become the key to bringing about the era of AIoT. This talk will show an exemplary AIoT service platform based on the FL system to support mobile dashcam video analysis. Then, we will demonstrate existing privacy and security threats in the FL system triggered by malicious end devices and the abnormal aggregator. Finally, we will discuss various perspectives regarding knowledge distillation, system and model heterogeneity in the current FL system, and future possible research directions.
Bio
Dr. Te-Chuan Chiu is currently an assistant professor at the Department of Computer Science, National Tsing Hua University (NTHU), Taiwan. Before that, he has served as a postdoctoral research scholar at the Research Center for Information Technology Innovation (CITI), Academia Sinica, Taiwan, from 2018 to 2022. He has been a research scholar at the Department of Electrical and Computer Engineering, University of California, Davis (UCD), USA in 2022. He received the Ph.D. degree in Computer Science and Information Engineering from National Taiwan University (NTU), Taiwan. His primary research interests include B5G/6G communications, edge intelligence/AI, fog/edge computing, and AIoT.
Recently, Dr. Chiu has earned the 2024 National Tsing Hua University (NTHU) EECS Junior Researcher Award, the Taiwan Association of Cloud Computing (TACC) Distinguished Young Scholar and Best Journal Paper Award, and the Pan Wen-Yuan Exploration Research Award.
Recently, Dr. Chiu has earned the 2024 National Tsing Hua University (NTHU) EECS Junior Researcher Award, the Taiwan Association of Cloud Computing (TACC) Distinguished Young Scholar and Best Journal Paper Award, and the Pan Wen-Yuan Exploration Research Award.