Toward provably private learning from federated data

Google Research · 1 week ago · 33 ● 2 sources

Google announced a TEE-based federated learning system that uses Trusted Execution Environments to let third parties verify and audit data anonymization and training logic while limiting what operators can see. The company says Gboard training for next-word prediction models previously took 1–2 months per model, and with the new system it achieves speedups that are limited by TEE resource availability. The shift changes the threat model by reducing reliance on trusting the server operator, while moving gradient computation scheduling and improving training accuracy and compute time via TEEs and public transparency logs.