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Esteemed machine learning visionary Professor Li Baochun explores the potential of federated learning

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Professor Li Baochun unveils his cutting-edge open-source federated learning framework “Plato” in the lecture.

Professor Li Baochun, an esteemed machine learning expert from the University of Toronto in Canada, delivered a highly informative lecture titled “Towards Production Federated Learning Systems” on June 5, 2023 (Monday). The lecture provided a comprehensive examination of the background and evolution of federated learning, with a particular emphasis on privacy considerations.


In his lecture, Professor Li highlighted the gap between research results on federated learning and their practical application in production systems. He shared his recent experiences with claims in the existing literature along the lines of privacy leakage attacks and demonstrated that their assumptions do not necessarily hold in production systems.


The Lecture also introduced more efficient ways to solve the "unlearning" problem, which is necessary due to regulatory constraints in production, such as the General Data Protection Regulation (GDPR). Furthermore, Professor Li introduced “Plato”, a new open-source federated learning framework that he designed from scratch in the past two years to be as close to production systems as possible, while using a minimum amount of computing resources.


Following the enlightening lecture, Professor Li demonstrated his commitment to fostering a vibrant intellectual atmosphere by actively engaging with faculty members and participants. His genuine enthusiasm created a dynamic environment that stimulated the exchange of ideas and encouraged the exploration of potential research collaborations.


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Professor Xu Jianliang, Head of the Department of Computer Science (right) expresses his heartfelt gratitude to Professor Li Baochun (left) for his invaluable insight and expertise in the field of machine learning.

As the Bell Canada Endowed Chair in Computer Engineering of the Department of Electrical and Computer Engineering at the University of Toronto, Professor Li holds a distinguished position in academia. His significant contributions to the field of machine learning, coupled with his extensive research portfolio in areas such as cloud computing, security and privacy, distributed machine learning, federated learning, and networking, have earned him the prestigious titles of the fellow of Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE).


Please click here for the full video of the Lecture and here for more photos.