Google trains Gboard with externally verifiable differential privacy using TEEs

Google Research moved federated learning gradient computation from phones into attested server-side trusted execution environments (TEEs). Access policies are published to Sigstore's Rekor log and binaries are reproducibly buildable, so central differential privacy can be checked externally. Gboard already uses it for English and Japanese next-word prediction.

Why it matters: Privacy claims in on-device learning have mostly rested on trust in the vendor. Making the guarantee auditable by third parties is a template that regulators and enterprise buyers could demand of other AI training pipelines handling personal data.