Title : Federated Learning and Distributed AI 

Qiang Yang

Abstract :
AI is advancing by leaps and bounds in learning algorithm development, but AI has many challenges when put to practice. One of the major challenges faced by AI is the serious lack of data, which has led to the inability of many good algorithm models to be effectively applied. In this talk, I will present federated learning as a solution designed to connect data silos while protecting user privacy and provide security. I will illustrate some theoretical advances and practical applications.

Biography :
Qiang Yang is Chief Artificial Intelligence Officer of WeBank, a Chair Professor of CSE Department of HKUST, Co-founder of 4Paradigm. He is the Conference Chair of AAAI-21, the Honorary Vice President of Chinese Association for Artificial Intelligence (CAAI) and the President of Hong Kong Society of Artificial Intelligence, Robotics (HKSAIR) and the President of IJCAI (2017-2019). He is a fellow of AAAI, ACM, CAAI, IEEE, IAPR, AAAS. He received the ACM SIGKDD Distinguished Service Award in 2017 and Wu Wenjun Award for Outstanding Contribution for Artificial Intelligence Science and Technology in 2019. His latest books are Transfer Learning and Federated Learning.