Google Research Moves Federated Learning Into TEEs: Gboard Now Trains With Externally Verifiable Differential Privacy
Google Research has announced a next-generation Federated Learning (FL) system built on Trusted Execution Environments (TEEs). The research team claims externally verifiable central differential privacy (DP) guarantees for FL for the first time. What Problem Does TEE-Based Federated Learning Solve? Google introduced Federated Learning FL in 2017. It powers next-word prediction and Smart Compose on Gboard, reply suggestions in Google Messages, and Smart Text Selection in Android. Earli
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Federated Learning is a system that trains AI models using data from many devices while keeping that data private. A trust gap existed in earlier versions because outside parties could not verify that device data was never logged or inspected by the server. The new system moves model training computations to Trusted Execution Environments, which are special secure areas on servers, and publishes access policies to a public transparency log so external auditors can verify what the server is allowed to do. This makes privacy guarantees externally verifiable for the first time, and it has enabled faster training of next-word prediction models for one application.
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