Keynote Session 5: Advances and Open Problems in Federated Learning, by Brendan McMahan (Google)

Abstract: Motivated by the explosive growth in federated learning research, 22 Google researchers and 36 academics from 24 institutions collaborated on a paper titled Advances and Open Problems in Federated Learning. In this talk, I will survey some of the main themes from the paper, particularly the defining characteristics and challenges of different FL settings. I will then briefly discuss some of the ways FL increasingly powers Google products, and also highlight several exciting FL research results from Google.

Biography: Brendan McMahan is a research scientist at Google, where he leads efforts on decentralized and privacy-preserving machine learning. His team pioneered the concept of federated learning, and continues to push the boundaries of what is possible when working with decentralized data using privacy-preserving techniques. Previously, he has worked in the fields of online learning, large-scale convex optimization, and reinforcement learning. Brendan received his Ph.D. in computer science from Carnegie Mellon University.