Abstract

Although artificial intelligence (AI) techniques such as deep learning has significantly promoted the development of many fields such as computer vision, natural language processing, and even biomedical data analytics, an increasing gap exists between what we can do technically and what is allowed to do, regarding the privacy concern, which drives us to develop reliable, secure, confidential and privacy-preserving AI solutions. In addition, it is quite challenging to construct a centralized database for certain biomedical applications since the data is distributed to different local users, which resulted in that the volume of local data is not insufficient to train reliable models for such applications. This talk will cover some preliminary work on developing a decentralized machine learning method based on swarm learning to resolve these issues. It will present details on swarm learning, our preliminary work on biomedical data analysis, and potential work on building decentralized methods based on the preliminary work for privacy-preserving biomedical applications.

Speaker Bio

Dr. Xishuang Dong is a member of CRI Center for Computational Systems Biology and Assistant Professor at Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU). He received B.S. degree in computer science and technique at Harbin University of Science and Technology, M.S. degree in computer software and theory at Harbin Engineering University, and Ph.D. in computer application at Harbin Institute of Technology. His research interests include: (1) machine learning based computational systems biology; (2) biomedical information processing; (3) deep learning for big data analysis; (4) natural language processing.