How Federated Learning Works: Training AI Without Seeing Your Data

How a keyboard can learn from what millions of people type without collecting their typing: the federated learning loop, the averaging trick that saves bandwidth, secure aggregation, differential privacy, and how sealed processors let outsiders check the server side.

0:27 Bring the model to the data

Instead of uploading data, each phone trains the shared model locally and sends back only a small, encrypted update that is averaged with many others.

Sources: Google Research (original)

1:50 The averaging trick

Federated Averaging lets phones do more work locally so far fewer rounds of communication are needed.

Sources: arXiv (original) · Google Research (original)

3:15 Hiding each phone in the crowd

Secure Aggregation uses cryptography so the server can only read the sum of many updates, never one phone's update.

Sources: IACR ePrint (original) · Google Research (original)

4:18 Adding noise on purpose

Differential privacy adds calibrated random noise during training so the final model cannot memorize any one person's data.

Sources: Google Research (original)

5:33 From trust us to check us

Running the server side inside trusted execution environments, with access policies in a public log, lets outsiders verify what code touches the data.

Sources: Google Research (original)

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Produced by Juan Pablo.