ML Seminar Series - On Heterogeneity in Federated Settings

Prof. Virginia Smith (Carnegie Mellon University)

"On Heterogeneity in Federated Settings"

A defining characteristic of federated learning is the presence of heterogeneity, i.e., that data and compute may differ significantly across the network. In this talk I show that the challenge of heterogeneity pervades the machine learning process in federated settings, affecting issues such as optimization, modeling, and fairness. In terms of optimization, I discuss FedProx, a distributed optimization method that offers robustness to systems and statistical heterogeneity. I then explore the role that heterogeneity plays in delivering models that are accurate and fair to all users/devices in the network. Our work here extends classical ideas in multi-task learning and alpha-fairness to large-scale heterogeneous networks, enabling flexible, accurate, and fair federated learning.


Bio: Virginia Smith is an assistant professor in the Machine Learning Department at Carnegie Mellon University. Her research interests span machine learning, optimization, and computer systems. Prior to CMU, Virginia was a postdoc at Stanford University, received a Ph.D. in Computer Science from UC Berkeley, and obtained undergraduate degrees in Mathematics and Computer Science from the University of Virginia.

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ML Seminars is a series of lectures on a variety of Machine Learning topics. Invited speakers for the series are leaders in their fields, hailing from respected research institutions worldwide. The ML Seminar Series is by the UT Austin Foundations of Data Science, an NSF Tripods Institute, and is administrated by WNCG.

http://sites.utexas.edu/mlseminars