Special Systems Design and Implementation Seminar

— 5:00pm

Location:
Traffic21 Classroom 6501 - Gates Hillman Centers

Speaker:
BRENDAN McMAHAN , Research Scientist
https://ai.google/research/people/author35837

Federated Learning, from Research to Practice

Federated Learning enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to collect and store the data in a central location. In this talk, I will discuss: how federated learning differs from more traditional machine learning paradigms; practical algorithms for federated learning that address the unique challenges of this setting; extensions to federated learning, including differential privacy, secure aggregation, and compression for model updates; federated learning applications and systems at Google; and finally a selection of exciting open problems and challenges in FL.

—

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.

Faculty Host: Phil Gibbons

Event Website:
https://www.pdl.cmu.edu/SDI/2019/090519-a.html

For More Information:
marcella@cs.cmu.edu

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