Google Research Moves Federated Learning Into TEEs: Gboard Now Trains With Externally Verifiable Differential Privacy
Google moved Gboard federated learning into attested TEEs with externally verifiable differential privacy.
Google Research described a federated learning design that computes gradients in attested server-side trusted execution environments instead of on phones. Access policies are published to Sigstore's Rekor transparency log and binaries are reproducibly buildable, so central differential privacy can be checked externally. Gboard already uses the system for English and Japanese next-word prediction.
- Gradient computation moved from phones into attested server-side TEEs.
- Access policies are logged in Sigstore Rekor and builds are reproducible.
- Gboard uses it for English and Japanese next-word prediction.
Google Research has unveiled a federated learning system that moves gradient computation from phones into attested server-side TEEs. Access policies are published to Sigstore's Rekor log and the binaries are reproducibly buildable, so central differential privacy can be checked externally. Gboard already uses it for English and Japanese next-word prediction. The post Google Research Moves Federated Learning Into TEEs: Gboard Now Trains With Externally Verifiable Differential Privacy appeared first on MarkTechPost.
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