SPS Webinar: The BRIDGE Framework: Robustness and Resilience in Decentralized Learning
Date: 28 August 2024
Time: 10:00 AM ET (New York Time)
Presenter(s): Dr. Waheed U. Bajwa
Download Article: Original article made publicly available on the day of the webinar for 48 hours.
Abstract
In many applications, machine learning involves managing data across multiple devices without the availability of a central server, necessitating a decentralized learning approach. In such settings, nodes are susceptible to failures from malfunctions or cyberattacks, which can undermine traditional learning algorithms. This webinar addresses the robustification of decentralized learning amidst Byzantine failures, where nodes can arbitrarily deviate, threatening system stability. Prior works have utilized ad-hoc methods akin to robust statistics; however, the presenter will propose a formal integration of robust statistical principles into the learning process for a more systematic approach. He introduces BRIDGE, a scalable Byzantine-resilient decentralized machine learning framework, designed to fortify resilience and offer structured analysis against Byzantine behaviors. BRIDGE comes with algorithmic and statistical convergence guarantees for both strongly convex and select nonconvex problems. Their experiments validate BRIDGE's scalability and effectiveness, underscoring its robustness and showcasing the benefits of incorporating robust statistics into decentralized learning systems formally.
Biography
Waheed U. Bajwa (M’98-SM’13) received the B.E degree in electrical engineering from the National University of Sciences & Technology, Pakistan, 2001, the M.S. and the Ph.D. degree in electrical engineering from the University of Wisconsin-Madison in 2005 and 2009 respectively.
He is currently a professor and graduate program director in the Department of Electrical and Computer Engineering and a member of the graduate faculty of the Department of Statistics at Rutgers University. Additionally, he has held positions in Princeton University, Duke University, and different technology startups. His research interests include statistical signal processing, high-dimensional statistics, machine learning, inverse problems, and networked systems.
Dr. Bajwa has received several research and teaching awards including the Army Research Office Young Investigator Award (2014), the National Science Foundation CAREER Award (2015), Rutgers Presidential Merit Award (2016), Rutgers Presidential Fellowship for Teaching Excellence (2017), Rutgers Engineering Governing Council ECE Professor of the Year Award (2016, 2017, 2019), Rutgers Warren I. Susman Award for Excellence in Teaching (2021), and Rutgers Presidential Outstanding Faculty Scholar Award (2022). 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.
