Robustness and Resilience in Decentralized Learning
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 paper 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, we propose a formal integration of robust statistical principles into the learning process for a more systematic approach. We introduce 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. Our experiments validate BRIDGE's scalability and effectiveness, underscoring its robustness and showcasing the benefits of incorporating robust statistics into decentralized learning systems formally.
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- ELB 2390
- University of Michigan-Dearborn
- Dearborn, Michigan
- United States 48128
- Building: Engineering Lab Building
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mjfarooq@umich.edu
- Co-sponsored by Department of Electrical and Computer Engineering, University of Michigan-Dearborn
- Starts 18 November 2025 05:00 AM UTC
- Ends 20 November 2025 05:00 AM UTC
- No Admission Charge
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Biography: Waheed U. Bajwa is a professor and graduate director in the Department of Electrical and Computer Engineering at Rutgers University–New Brunswick, and a member of the graduate faculty in the Department of Statistics. He received his PhD in electrical engineering from the University of Wisconsin–Madison in 2009. Over the course of his career, he has held various positions in industry, as well as academic appointments at Princeton University and Duke University. His research interests span statistical signal processing, high-dimensional statistics, machine learning, inverse problems, and networked systems. In recognition of his contributions to these areas, he was elevated to the rank of IEEE Fellow in 2025.
Dr. Bajwa has received numerous honors for both research and teaching, including the National Science Foundation CAREER Award, the Army Research Office Young Investigator Award, the Rutgers Presidential Outstanding Faculty Scholar Award, and the Warren I. Susman Award for Excellence in Teaching. He has co-authored award-winning papers at IEEE workshops and received recognition from the Cancer Institute of New Jersey for collaborative research. He has served in a range of editorial and leadership roles within the IEEE Signal Processing Society, including currently serving as Senior Editorial Board Member of the IEEE Signal Processing Magazine and Senior Area Editor of the IEEE Open Journal of Signal Processing. Outside the IEEE Signal Processing Society, he also serves as Associate Editor of the IEEE Transactions on Information Theory. In addition, he has contributed extensively to technical conferences and workshops and has held several elected positions on IEEE technical committees.
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