SPIRAL Seminar: Fault-tolerant federated and distributed machine learning

Speaker: Sanmi Koyejo (University of Illinois at Urbana-Champaign)
Title: Fault-tolerant federated and distributed machine learning
Time: Friday, 11/13, 12 pm ET/ 11 am CST/ 9 am PT
Location: https://northeastern.zoom.us/j/95550946164
Student Host: Peng Wu
Faculty Host: Stratis loannidis

Abstract:
Machine learning (ML) models are routinely trained and deployed among devices that are susceptible to hardware/software/communication errors and/or security concerns. Examples include geo-distributed datacenters with non-negligible communication latency, groups of mobile devices or Internet of Things (IoT), and volunteer ML computing. For such settings, distributed training typically consists of separate updates interleaved with aggregation. To this end, I will outline novel aggregation schemes for fault-tolerant federated learning and distributed training via stochastic gradient descent. The proposed aggregation schemes are shown to be provably robust to worst-case errors from a large fraction of arbitrarily malicious workers (aka Byzantine errors), with minimal effect on convergence rates. Empirical evaluation in a variety of real-world setting further highlights the performance of the proposed aggregation strategies.

Biography:
Sanmi Koyejo an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo’s research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to neuroscience and biomedical imaging. Koyejo has been the recipient of several awards including a best paper award from the conference on uncertainty in artificial intelligence (UAI), a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping (OHBM). Koyejo serves on the board of the Black in AI organization. http://sanmi.cs.illinois.edu/bio.html