Privacy for users is a central goal of cross-device federated learning. This talk begins with a quick overview of federated learning and key privacy principles. We then deep-dive into some recent advances in providing stronger anonymization properties for cross-device federated learning, including the DP-FTRL algorithm that was used to launch a production neural-language model trained with a user-level differential privacy guarantee.
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. McMahan received his Ph.D. in computer science from Carnegie Mellon University.
