报告题目：Towards Pervasive and Trustworthy Edge Intelligence in Next-Generation Wireless Networks
报 告 人：Yuanxiong Guo (郭远雄)
报告地点：线上腾讯会议：192-595-086
报告华体会网页版登陆入口：2022年10月27日10:00
主 持 人：邓晓衡
报告简介：The proliferation of Internet-of-things (IoT) devices such as smartphones, surveillance cameras, and vehicles, each equipped with rich sensing, computation, and storage resources, leads to tremendous data being generated on a daily basis closer to the data source at the network edge. At the same time, artificial intelligence (AI) and machine learning (ML) are advancing rapidly and enable efficient knowledge extraction from large volumes of data. The convergence of IoT and AI/ML leads to many emerging edge intelligence applications with significant economic and societal impacts such as autonomous driving, augmented reality, real-time video analytics, mobile healthcare, and smart manufacturing. However, the large and continuously streaming data generated by these applications must be processed efficiently and securely enough to support real-time learning and decision making based on these data. In this talk, I will present our recent work on enabling pervasive and trustworthy edge intelligence in next-generation wireless networks, ranging from network-aware distributed learning algorithm design to differentially private distributed learning.
讲者简介：Yuanxiong Guo received the B.Eng. degree in electronics and information engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2009, and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Florida, Gainesville, FL, USA, in 2012 and 2014, respectively. He is currently an Associate Professor in the Department of Information Systems and Cyber Security at the University of Texas at San Antonio, San Antonio, TX, USA. His current research interests include machine learning, data-driven decision making, security and privacy with applications to Internet of Things and edge computing. He is on the Editorial Board of IEEE Transactions on Vehicular Technology and has served as the track co-chairs for IEEE VTC 2021-Fall and PST 2022. He is a recipient of the Best Paper Award in the IEEE Global Communications Conference 2011. His research has been supported by NSF, AFRL, NASA, NVIDIA, and local industry.
