Attend the Women in ML Symposium on December 7 Register now


Stay organized with collections Save and categorize content based on your preferences.

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

Used in the notebooks

Used in the tutorials

model_fn A no-arg function that returns a tff.learning.Model, or an instance of a tff.learning.models.FunctionalModel. When passing a callable, the callable 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@CLIENTS) and have empty state. If set to default None, the server model is broadcast to the clients using the default tff.federated_broadcast.
metrics_aggregator An optional function that takes in the metric finalizers (i.e., tff.learning.Model.metric_finalizers()) and a tff.types.StructWithPythonType of the unfinalized metrics (i.e., the TFF type of tff.learning.Model.report_local_unfinalized_metrics()), and returns a federated TFF computation of the following type signature local_unfinalized_metrics@CLIENTS -> aggregated_metrics@SERVER. If None, uses tff.learning.metrics.sum_then_finalize, which returns a federated TFF computation that sums the unfinalized metrics from CLIENTS, and then applies the corresponding metric finalizers at SERVER.
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.