Wed, Feb 16, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Tianyi Chen, Assistant Professor, Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute
Talk Title: Scalable and Trustworthy Learning for Distributed Intelligence
Abstract: The past decade has witnessed the revival of artiï¬cial intelligence (AI) and machine learning (ML) in almost every branch of science and technology. The \"fuel\" to AI and ML is supplied by the surge of data and computing power. Today, data and computing power are distributed among wireless devices and companies that we term data owners. Due to the pressing need for data in AI/ML tasks and the increasing concerns on data privacy, a sizeable number of AI/ML tasks will be executed across networked data owners with the vision of distributed intelligence.
In this talk, I will use federated learning (FL) as an example of distributed intelligence. I will highlight its key challenges when it interacts with wireless networks such as efficiency, privacy, security, and robustness. I will focus on two aspects of scalable and trustworthy FL - efficiency and privacy. From a unified view of information correlation among iterative FL updates, I will elaborate i) how we can leverage such correlation to improve the efficiency of FL; and ii) how such correlation may be leveraged by a malicious third party to risk data privacy. Our methods are simple to implement, and they come with rigorous performance guarantees. I will conclude this talk by highlighting a few directions that I will pursue towards distributed intelligence beyond FL.
Biography: Tianyi Chen is an Assistant Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), where he is jointly supported by the RPI - IBM Artificial Intelligence Research Partnership. Dr. Chen received his B. Eng. degree in Electrical Engineering from Fudan University in 2014, and the Ph.D. degree in Electrical and Computer Engineering from the University of Minnesota in 2019. During 2017-2018, he has been a visiting scholar at Harvard University, University of California, Los Angeles, and University of Illinois Urbana-Champaign. Dr. Chen\'s research focuses on theoretical and algorithmic foundations of optimization, machine learning, and statistical signal processing, with applications in networked computing systems such as wireless and IoT systems.
Dr. Chen is the inaugural recipient of IEEE Signal Processing Society Best PhD Dissertation Award in 2020 and a recipient of NSF CAREER Award in 2021. He is also the co-author of several best paper awards such as the Best Student Paper Award at the NeurIPS Federated Learning Workshop in 2020 and at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) in 2021.
Host: Dr. Salman Avestimehr, email@example.com
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher