Electrical and Computer Engineering

Optimization in the federated setting

September 13, 2018

4:00 p.m. ET

Scaife Hall, Room 125

Abstract

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.

Bio

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.

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