In 2017, Google introduced Federated Learning (FL) a machine learning technique that trains models across decentralized, private data. It has been used to power everyday helpful features, including next-word prediction and Smart Compose on Gboard, reply suggestions in Google Messages, and Smart Text Selection in Android.
Our FL systems development is guided by four essential privacy principles: (1) data minimization, (2) data anonymization, (3) transparency and control, and (4) verifiability and auditability. Years of research development on anonymization have led to strong differential privacy (DP) guarantees for production models through algorithms like matrix factorization DP-FTRL (MF-DP-FTRL) and distributed differential privacy coupled with Secure Aggregation. In 2025, we introduced an evolved definition of FL centered on these four principles:
Federated learning (FL) is a machine learning setting where multiple entities (clients) collaborate in solving a machine learning problem, under the coordination of a service provider. A complete FL system should enable clients to maintain full control over their data, the set of workloads allowed to access their data, and the anonymization properties of those workloads. FL systems should provide appropriate transparency and control to the users whose data is managed by FL clients.
In “Toward provably private learning from federated data”, we announce the next generation of our FL system, which leverages Trusted Execution Environments (TEEs) to provide fully verifiable and auditable data anonymization guarantees. Logic that runs in TEEs is remotely attestable (third parties can verify the logic that is being executed), and it also gains confidentiality (its internal state cannot be observed) and integrity (the logic cannot be disrupted), subject to current-generation TEE limitations. Our new TEE-based FL system builds on these properties which TEEs offer at the level of a single machine to form a fully verifiable end-to-end FL system that achieves stronger privacy guarantees and improved accuracy. Gboard has already adopted the new system and is benefiting from substantially faster compute times than our previous FL system.
