[danieljanes]

Hi there - the data never moves if you train a model using federated learning. It stays on user devices or in organizational silos. After the training, you have the model parameters of the model on the server, without the server having ever seen a single data example.

After the training, you can deploy the model in different ways. If you want to use it on device (or in one of the organizational silos), you can send the final model parameters there and deploy it locally. Or you just deploy the model on the server behind an API. It all depends on the use case.

Hope that helps, I'm happy to provide more details.