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ECE Colloquium Series – Professor Waheed U. Bajwa
April 5 @ 3:30 pm - 5:00 pm
As part of the *Eleanore Hale Wilson Lecture Series, ECE is proud to present:
Byzantine-resilient Distributed Machine Learning for the Internet of Things
Professor Waheed U. Bajwa
Host: Professor Jarvis Haupt
Distributed machine learning algorithms enable processing of datasets that are distributed over a network without gathering the data at a centralized location. While efficient
distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional indeed happen in the real world. In this talk, we focus on the problem of Byzantine failures, which are the hardest to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional distributed learning tasks. In this talk, we discuss the theoretical characteristics and experimental performance of a few Byzantine-resilient algorithms that have been developed in our lab for high-dimensional distributed machine learning in fault-prone networks. In particular, we show that a single Byzantine node in the network can lead to failures of most state-of-the-art distributed learning algorithms; in contrast, our developed algorithms are capable of handling multiple Byzantine failures in the network without noticeable reduction in performance.
Waheed U. Bajwa received BE (with Honors) degree in electrical engineering from the National University of Sciences and Technology, Pakistan in 2001, and MS and PhD degrees in electrical engineering from the University of Wisconsin-Madison in 2005 and 2009, respectively. He was a Postdoctoral Research Associate in the Program in Applied and Computational Mathematics at Princeton University from 2009 to 2010, and a Research Scientist in the Department of Electrical and Computer Engineering at Duke University from 2010 to 2011. He is currently an Associate Professor in the Department of Electrical and Computer Engineering at Rutgers University. His research interests include statistical signal processing, high-dimensional statistics, machine learning, networked systems, and inverse problems.
Dr. Bajwa has received a number of awards in his career including the Best in Academics Gold Medal and President’s Gold Medal in Electrical Engineering from the National University of Sciences and Technology (2001), the Morgridge Distinguished Graduate Fellowship from the University of Wisconsin-Madison (2003), the Army Research Office Young Investigator Award (2014), the National Science Foundation CAREER Award (2015), Rutgers University’s Presidential Merit Award (2016), Rutgers Engineering Governing Council ECE Professor of the Year Award (2016, 2017), and Rutgers University’s Presidential Fellowship for Teaching Excellence (2017). He is a co-investigator on the work that received the Cancer Institute of New Jersey’s Gallo Award for Scientific Excellence in 2017, a co-author on papers that received Best Student Paper Awards at IEEE IVMSP 2016 and IEEE CAMSAP 2017 workshops, and a Member of the Class of 2015 National Academy of Engineering Frontiers of Engineering Education Symposium. He served as an Associate Editor of the IEEE Signal Processing Letters (2014 – 2017), co-guest edited a special issue of Elsevier Physical Communication Journal on “Compressive Sensing in Communications” (2012), co-chaired CPSWeek 2013 Workshop on Signal Processing Advances in Sensor Networks and IEEE GlobalSIP 2013 Symposium on New Sensing and Statistical Inference Methods, and served as the Publicity and Publications Chair of IEEE CAMSAP 2015 and General Chair of the 2017 DIMACS Workshop on Distributed Optimization, Information Processing, and Learning. He is currently Technical Co-Chair of the IEEE SPAWC 2018 Workshop, a Senior Member of the IEEE, and serves on the MLSP, SAM, and SPCOM Technical Committees of the IEEE Signal Processing Society.
*Established in 2009, the Eleanore Hale Wilson Fund supports engineering field leaders for travel to Minnesota to share their expertise and discoveries with University of Minnesota graduate students, faculty, and alumni. The fund also supports the receptions held in honor of each speaker.