Google Keynote: Federated Aggregation and Privacy

A Google TechTalk, presented by Dan Ramage, Brendan McMahan, & Kallista Bonawitz, 2021/11/8 ABSTRACT: 3 Google researchers talk about the state of the art in federated aggregations and privacy. About the Speakers Brendan McMahan, Google - Brendan McMahan has worked in the fields of online learning, large-scale convex optimization, and reinforcement learning. He received his Ph.D. in computer science from Carnegie Mellon University. Brendan is currently a researcher at Google, focusing on decentralized and privacy-preserving machine learning. Brendan's 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. Daniel Ramage, Google - Daniel Ramage has worked in the fields of natural language processing, machine intelligence, and mobile systems. He received his Ph.D. from Stanford University. Daniel is currently a researcher at Google, focusing on decentralized and privacy-preserving machine learning. Daniel's 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. Kallista Bonawitz, Google - Kallista Bonawitz previously led the planning, simulation, and control team for Project Loon at Alphabet’s X and co-founded Navia Systems (a probabilistic computing startup later acquired by Salesforce as Prior Knowledge). She received her Ph.D. in computer science from the Massachusetts Institute of Technology. Kallista is currently a researcher at Google, focusing on decentralized and privacy-preserving machine learning. Kallista's 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. For more information about the workshop: https://events.withgoogle.com/2021-workshop-on-federated-learning-and-analytics/#content