An Overview of Federated Machine Learning from Industry Experts

Federated learning allows multiple parties to collaboratively build and use machine learning models on distributed and secure data sources while preserving privacy. In this event, you will hear from two of the top minds in Federated Machine Learning (FML) as they discuss identified challenges and potential impacts, and speak to the newly published standard IEEE 3652.1-2020 about FML.

Prof. Qiang Yang, Fellow of Royal Society of Canada (RSC) and Canadian Academy of Engineering (CAE), Chief Artificial Intelligence Officer of WeBank, and Chair Professor of Computer Science and Engineering Department at Hong Kong University of Science and Technology (HKUST) will provide an overview of the standard, while Dr. Blaise Arcas, VP and Fellow at Google Research, whose Google Brain team invented the concept of FL, offers an update on the status of FML and the impact so far.

Together they will have a dynamic discussion on the challenges and obstacles keeping FML from reaching its full potential and where standards could help address them. They will offer their vision of the FML roadmap and what it will take to accomplish it--a momentous task, but for these two entrepreneurial spirits, it's an exciting task with infinite possibilities.

Join us for this exciting event on 4 January 2022.  

Speakers

Learn from, engage with, and discuss important topics with these experts:

What You'll Learn:

  • Gain insight on one of the most exciting technologies--Federated Machine Learning--of our time and its potential use cases. 

Who Should Participate:

  • AI scientists and engineers passionate about federated learning;
  • Social scientists and engineers who are interested in exploring federated learning’s potential in to enabling privacy protection and beyond;  
  • Anyone interested in learning more about Federated Machine Learning and its potential use cases.

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