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Home | Seminars and Symposia | Past seminars/symposia: Friday, July 6, 2018

DTC Seminar Series

Optimization in the Federated Setting


Virginia Smith
Carnegie Mellon University

Friday, July 6, 2018
1:30 p.m. seminar

401/402 Walter Library

Virginia Smith

The nascent field of federated learning explores training statistical models over massive networks of distributed devices. This task poses novel challenges in distributed optimization, including issues related to high communication, stragglers, and fault tolerance. By marrying systems-level constraints and optimization techniques, we provide robust methods and order-of-magnitude speedups for solving machine learning problems in this burgeoning setting. We corroborate empirical results with theoretical guarantees that expose systems parameters to give further insight into empirical performance.


Virginia Smith is an assistant professor in Electrical and Computer Engineering at Carnegie Mellon University, and an affiliated faculty member in the Machine Learning Department. Her research interests are at the intersection of machine learning, optimization, and distributed systems. She has been the recipient of the NSF Graduate Research Fellowship, Google Anita Borg Memorial Scholarship, NDSEG Fellowship, and MLConf Industry Impact Award. Prior to CMU, Virginia received a Ph.D. from UC Berkeley and undergraduate degrees from the University of Virginia.