Federated Learning is at the intersection of AI and privacy computing. How to make federated learning more trustworthy, effective and efficient is the focus of future industry and academia. In this talk, Prof Yang will review the progress and lay out challenges of trustworthy federated learning and federated large language models in the future.
Qiang Yang is a Fellow of Canadian Academy of Engineering (CAE) and Royal Society of Canada (RSC), Chief Artificial Intelligence Officer of WeBank and Chair Professor of CSE Department of Hong Kong Univ. of Sci. and Tech. He is the Conference Chair of AAAI-21, President of Hong Kong Society of Artificial Intelligence and Robotics (HKSAIR), the President of Investment Technology League (ITL) and Open Islands Privacy-Computing Open-source Community, and former President of IJCAI (2017-2019). He is a fellow of AAAI, ACM, IEEE and AAAS. His research interests include transfer learning and federated learning. He is also the founding EiC of two journals: IEEE Transactions on Big Data and ACM Transactions on Intelligent Systems and Technology. His latest books are Transfer Learning, Federated Learning, Privacy-preserving Computing and Practicing Federated Learning.