tff.learning.metrics.sum_then_finalize

Creates a TFF computation that aggregates metrics via sum_then_finalize.

tff.learning.metrics.sum_then_finalize(
    metric_finalizers: Union[tff.learning.metrics.MetricFinalizersType, tff.learning.metrics.FunctionalMetricFinalizersType],
    local_unfinalized_metrics_type: Optional[tff.types.StructWithPythonType] = None
) -> tff.Computation

The returned federated TFF computation is a polymorphic computation that accepts unfinalized client metrics, and returns finalized, summed metrics placed at the server. Invoking the polymorphic computation will initiate tracing on the argument and will raise a ValueError if the keys (i.e., metric names) in metric_finalizers are not the same as those of the argument the polymorphic method is invoked on.

Note: invoking this computation outside of a federated context (a method decorated with tff.federated_computation) will require first wrapping it in a concrete, non-polymorphic tff.Computation with appropriate federated types.

Args
metric_finalizers	Either the result of tff.learning.models.VariableModel.metric_finalizers (an OrderedDict of callables) or the tff.learning.models.FunctionalModel.finalize_metrics method (a callable that takes an OrderedDict argument). If the former, the keys must be the same as the OrderedDict returned by tff.learning.models.VariableModel.report_local_unfinalized_metrics. If the later, the callable must compute over the same keyspace of the result returned by tff.learning.models.FunctionalModel.update_metrics_state.
local_unfinalized_metrics_type	Unused, will be removed from the API in the future.

Returns
A federated TFF computation that sums the unfinalized metrics from CLIENTS, and applies the correponding finalizers at SERVER.

Raises
TypeError	If the inputs are of the wrong types.