Abstract: Machine learning has demonstrated great promises in various fields, e.g., finance, self-driving and healthcare. The success of deep learning stems from the availability of big training data and massive computation power. However, in many applications, training data are generated by distributed devices owned by individuals, who hesitate to share their data that expose privacy. Moreover, it becomes difficult to aggregate these data to a single computing site for centralized training due to the increasing data size. In this talk, we will present a paradigm complementary to the cloud-based methods for machine learning in the cloud-edge environment. It is proposed and developed for moving the training and inference to the edge environment to serve the delay-sensitive and privacy-sensitive applications, of which the data cannot be gathered to the cloud. While edge learning has great potential for many intelligent applications, e.g., smart cities and self-driving cars, it is quite challenging to realize it in an efficient and secure manner due to the inherent characteristics of the cloud-edge environment. We will present the opportunities, open challenges and possible solutions of edge learning in this talk.

Bio: Peng Li received his BS degree from Huazhong University of Science and Technology, China, in 2007, the MS and PhD degrees from the University of Aizu, Japan, in 2009 and 2012, respectively. Dr. Li is currently an Associate Professor in the University of Aizu, Japan. His research interests mainly focus on cloud computing, Internet-of-Things, big data systems, as well as related wired and wireless networking problems. Dr. Li has published over 100 technical papers on prestigious journals and conferences. He won the Young Author Award of IEEE Computer Society Japan Chapter in 2014. He won the Best Paper Award of IEEE TrustCom 2016. He supervised students to win the First Prize of IEEE ComSoc Student Competition in 2016. Dr. Li is the editor of IEICE Transactions on Communications. He is a member of IEEE.