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tff.learning.build_federated_evaluation

Builds the TFF computation for federated evaluation of the given model.

tff.learning.build_federated_evaluation(
    model_fn,
    broadcast_process: Optional[tff.templates.MeasuredProcess] = None,
    use_experimental_simulation_loop: bool = False
)

Used in the notebooks

Used in the tutorials

model_fn A no-arg function that returns a tff.learning.Model. This method must not capture TensorFlow tensors or variables and use them. The model must be constructed entirely from scratch on each invocation, returning the same pre-constructed model each call will result in an error.
broadcast_process a tff.templates.MeasuredProcess that broadcasts the model weights on the server to the clients. It must support the signature (input_values@SERVER -> output_values@CLIENT) and have empty state. If set to default None, the server model is broadcast to the clients using the default tff.federated_broadcast.
use_experimental_simulation_loop Controls the reduce loop function for input dataset. An experimental reduce loop is used for simulation.

A federated computation (an instance of tff.Computation) that accepts model parameters and federated data, and returns the evaluation metrics as aggregated by tff.learning.Model.federated_output_computation.