EE Colloquium: Adversary-Resilient Decentralized Machine Learning

Abstract: Decentralized machine learning algorithms enable processing of datasets that are distributed over a network without gathering the data at a centralized location. While efficient decentralized 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 decentralized algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional decentralized 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 decentralized 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 decentralized learning algorithms; in contrast, our developed algorithms are capable of handling multiple Byzantine failures in the network without noticeable reduction in performance.


Biography: 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 has been with Rutgers University since 2011, where he is currently an associate professor in the Department of Electrical and Computer Engineering and an associate member of the graduate faculty of the Department of Statistics and Biostatistics. His research interests include statistical signal processing, high-dimensional statistics, machine learning, harmonic analysis, inverse problems, and networked systems.

Dr. Bajwa has received a number of awards in his career including 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, 2019), and Rutgers University's Presidential Fellowship for Teaching Excellence (2017). He is a co-investigator on a 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.

 

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Media Contact: Vishal Monga

 
 

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The School of Electrical Engineering and Computer Science was created in the spring of 2015 to allow greater access to courses offered by both departments for undergraduate and graduate students in exciting collaborative research in fields.

We offer B.S. degrees in electrical engineering, computer science, computer engineering and data science and graduate degrees (master's degrees and Ph.D.'s) in electrical engineering and computer science and engineering. EECS focuses on the convergence of technologies and disciplines to meet today’s industrial demands.

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