tff.learning.framework.build_model_delta_optimizer_process

Constructs tff.templates.IterativeProcess for Federated Averaging or SGD.

tff.learning.framework.build_model_delta_optimizer_process(
    model_fn: tff.learning.Model,
    model_to_client_delta_fn: Callable[[Callable[[], model_lib.Model]], ClientDeltaFn],
    server_optimizer_fn: tff.learning.optimizers.Optimizer,
    *,
    broadcast_process: Optional[tff.templates.MeasuredProcess] = None,
    model_update_aggregation_factory: Optional[factory.AggregationFactory] = None,
    metrics_aggregator: Optional[Callable[[model_lib.MetricFinalizersType, computation_types.
        StructWithPythonType], computation_base.Computation]] = None
) -> tff.templates.IterativeProcess

This provides the TFF orchestration logic connecting the common server logic which applies aggregated model deltas to the server model with a ClientDeltaFn that specifies how weight_deltas are computed on device.

Args
`model_fn`	A no-arg function that returns a `tff.learning.Model`.
`model_to_client_delta_fn`	A function from a `model_fn` to a `ClientDeltaFn`.
`server_optimizer_fn`	A `tff.learning.optimizers.Optimizer` or a no-arg function that returns a `tf.keras.optimizers.Optimizer`.
`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)`. If set to default None, the server model is broadcast to the clients using the default tff.federated_broadcast.
`model_update_aggregation_factory`	An optional `tff.aggregators.WeightedAggregationFactory` or `tff.aggregators.UnweightedAggregationFactory` that contstructs `tff.templates.AggregationProcess` for aggregating the client model updates on the server. If `None`, uses `tff.aggregators.MeanFactory`.
`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`.

Returns
A `tff.templates.IterativeProcess`.

Raises
`ProcessTypeError`	If `broadcast_process` does not conform to the signature of broadcast (SERVER->CLIENTS).

tff.learning.framework.build_model_delta_optimizer_process

Args

Returns

Raises