UMN Machine Learning Seminar: Secure Model Aggregation in Federated Learning

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Salman Avestimehr (University of Southern California), will be giving a talk titled "Secure Model Aggregation in Federated Learning."

Abstract

Federated learning (FL) has emerged as a promising approach for distributed machine learning over edge devices, in order to strengthen data privacy, reduce data migration costs, and break regulatory restrictions. A key component of FL is "secure model aggregation", which aims at protecting the privacy of each user’s individual model, while allowing their global aggregation. This problem can be viewed as a privacy-preserving multi-party computing, but with two interesting twists: (1) some users may drop out during the protocol (due to poor connectivity, low battery, unavailability, etc); (2) there is potential for multi-round privacy leakage, even if each round is perfectly secure. In this talk, I will first provide a brief overview of FL, then discuss several recent results on secure model aggregation, and finally end the talk by highlighting a few open problems in the area.

Biography

Salman Avestimehr is a Dean's Professor, the inaugural director of the USC-Amazon Center on Secure and Trusted Machine Learning (Trusted AI), and the director of the Information Theory and Machine Learning (vITAL) research lab at the Electrical and Computer Engineering Department of University of Southern California. He is also an Amazon Scholar at Alexa AI. He received his Ph.D. in 2008 and M.S. degree in 2005 in Electrical Engineering and Computer Science, both from the University of California, Berkeley. Prior to that, he obtained his B.S. in Electrical Engineering from Sharif University of Technology in 2003. His research interests include information theory, and large-scale distributed computing and machine learning, secure and private computing/learning, and federated learning.

Dr. Avestimehr has received a number of awards for his research, including the James L. Massey Research & Teaching Award from IEEE Information Theory Society, an Information Theory Society and Communication Society Joint Paper Award, a Presidential Early Career Award for Scientists and Engineers (PECASE) from the White House (President Obama), a Young Investigator Program (YIP) award from the U. S. Air Force Office of Scientific Research, a National Science Foundation CAREER award, a USC Mentoring Award, and the David J. Sakrison Memorial Prize, and several Best Paper Awards at Conferences. He has been an Associate Editor for IEEE Transactions on Information Theory and a general Co-Chair of the 2020 International Symposium on Information Theory (ISIT). He is a fellow of IEEE.