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Google’s federated learning breakthrough set the pace: researchers moved central differential privacy guarantees into the messy reality of on-device training by wrapping the process in a Trusted Execution Environment (TEE). The result is “externally verifiable” privacy—meaning the math can be audited rather than merely asserted. Google says it used the setup in Gboard to train next-word prediction models, a pragmatic choice that matters because it ties privacy guarantees to a product people actually type on, not just a benchmark spreadsheet.
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