Toward provably private learning from federated data

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.

Building a verifiably private FL system out of individual TEEs

Our TEE-based FL system builds on techniques developed in our earlier work on confidential federated analytics and provably private insights.

The system coordinates four core operational concepts:

For more details on the TEE-based FL system design please see our whitepaper, Toward provably private learning from federated data.

How TEE-based FL strengthens privacy

Public transparency log and reproducible builds

The KMS and data processing binaries used in our FL system can be reproducibly built from open source code published in the Confidential Federated Compute Github repository.