Abstract Federated learning (FL) is a field of ML focused on learning from decentralized data (e.g., across a population of mobile phones). FL at Google has undergone a transformation over the last several years, in order to (a) enable privacy protections which are verifiable by external parties and (b) support LLM-powered data science workflows. In this talk I'll present an overview of our new FL paradigm which leverages trusted execution environments ('TEEs'). I'll share what TEE-based analytics and learning workflows are now enabled, as well as research threads motivated by these new capabilities. Finally, I'll share work by my teammates and I outside of (and complementary to) privacy, on efficient on-device ML. Speaker bio Sean is a Staff Research Scientist at Google Research. His rese