tff.learning.templates.build_model_delta_client_work

Creates a ClientWorkProcess for federated averaging.

tff.learning.templates.build_model_delta_client_work(
    model_fn: Callable[[], tff.learning.models.VariableModel],
    optimizer: tff.learning.optimizers.Optimizer,
    client_weighting: tff.learning.ClientWeighting,
    metrics_aggregator: Optional[tff.learning.metrics.MetricsAggregatorType] = None,
    *,
    loop_implementation: tff.learning.LoopImplementation = tff.learning.LoopImplementation.DATASET_REDUCE
) -> tff.learning.templates.ClientWorkProcess

model_fn A no-arg function that returns a tff.learning.models.VariableModel. 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.
optimizer A tff.learning.optimizers.Optimizer.
client_weighting A tff.learning.ClientWeighting value.
metrics_aggregator A function that takes in the metric finalizers (i.e., tff.learning.models.VariableModel.metric_finalizers()) returns a tff.Computation for aggregating the unfinalized metrics. If None, this is set to tff.learning.metrics.sum_then_finalize.
loop_implementation Changes the implementation of the training loop generated. See tff.learning.LoopImplementation for more details.

A ClientWorkProcess.