Data Science and Analytics Webinar 2021

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Advances Federated Learning: Challenges, Opportunities, and Real-World Frameworks

9:30am - 10:30am
Zoom ID: 924 5629 1671, Password: 123321

In the era of Internet of Things, edge computing, and Big Data, large volumes of data has been generated by millions of mobile devices and sensors at the edge of the Internet on a daily basis. Resolving the conflict between the need for analyzing such data and the requirements for preserving user privacy has always been a balancing act. Federated learning, as a recent distributed computing paradigm involving both the edge and the cloud, has been touted in the recent years as a way to achieve a balanced tradeoff between utilizing data and preserving data privacy. It offers us the best of both worlds: privacy-preserving training of machine learning models. Yet, by performing local training and collecting model updates from edge devices, federated learning incurred a large amount of communication overhead, and has to address the problems of systems and statistical heterogeneity. In this talk, we begin with an instant primer of the current challenges in federated learning, present our recent work related to statistical heterogeneity, and introduce our open-source real-world framework, called Plato, for scalable federated learning research. We will also provide insights and an outlook on opportunities for future research. 

講者/ 表演者:
Dr. Baochun Li
Department of Electrical and Computer Engineering, University of Toronto

Baochun Li received his B.Engr. degree from the Department of Computer Science and Technology, Tsinghua University, China, in 1995 and his M.S. and Ph.D. degrees from the Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, in 1997 and 2000. Since 2000, he has been with the Department of Electrical and Computer Engineering at the University of Toronto, where he is currently a Professor. He holds the Bell Canada Endowed Chair in Computer Engineering since August 2005. His research interests include cloud computing, distributed systems, datacenter networking, and wireless systems.Dr. Li has co-authored more than 410 research papers, with a total of over 21000 citations, an H-index of 83 and an i10-index of 280, according to Google Scholar Citations. He was the recipient of the IEEE Communications Society Leonard G. Abraham Award in the Field of Communications Systems in 2000. In 2009, he was a recipient of the Multimedia Communications Best Paper Award from the IEEE Communications Society, and a recipient of the University of Toronto McLean Award. He is a member of ACM and a Fellow of IEEE. 

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Information Hub, HKUST(GZ)
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