Information Hub Distinguished Lecture Series - Trustworthy Federated Learning and Federated Large Language Models 可信联邦学习与联邦大模型
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 my talk, I will review the progress and lay out challenges of Trustworthy Federated Learning and Federated Large Language Models in the future.
联邦学习是人工智能和隐私计算的重要交集。如何使联邦学习平衡优化以达到安全可信和高效高质的效果，并且和大模型无缝连接，是今后产业和学界关注的重点。我在讲座中将系统回顾联邦学习的进展和挑战，并展望几个重要发展方向。
Prof. 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 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.
杨强，加拿大工程院及加拿大皇家学院两院院士，微众银行首席人工智能官，香港科技大学计算机与工程系讲座教授和前系主任，AAAI-2021大会主席，国际人工智能联合会（IJCAI）理事会前主席，香港人工智能与机器人学会（HKSAIR）理事长，智能投研技术联盟（ITL）和开放群岛开源社区(OI)主席，ACM TIST 和IEEE TRANS on BIG DATA创始主编，CAAI，AAAI，ACM，IEEE，AAAS等多个国际学会Fellow。领衔全球迁移学习和联邦学习研究及应用，著作包括《迁移学习》、《联邦学习》、《隐私计算》和《联邦学习实战》等。