Google Moves Federated Learning Into Trusted Execution Environments With Verifiable Differential Privacy for Gboard

Google Research has deployed federated learning inside trusted execution environments (TEEs) for Gboard, enabling externally verifiable differential privacy guarantees during on-device model training. This means third parties can now cryptographically verify that the differential privacy mechanisms are actually applied — not just claimed — during training, closing a significant auditability gap in privacy-preserving ML. The architecture combines the decentralization of federated learning with hardware-level attestation from TEEs, giving both users and regulators a stronger basis for trust. For developers working on privacy-sensitive ML pipelines, this sets a new bar for what verifiable privacy guarantees can look like in production. The deployment at Gboard's scale demonstrates this approach is engineering-ready, not just theoretical.