tff.learning.algorithms.build_weighted_fed_avg

Builds a learning process that performs federated averaging.

tff.learning.algorithms.build_weighted_fed_avg(
    model_fn: Union[Callable[[], tff.learning.models.VariableModel], tff.learning.models.FunctionalModel],
    client_optimizer_fn: tff.learning.optimizers.Optimizer,
    server_optimizer_fn: Optional[tff.learning.optimizers.Optimizer] = None,
    *,
    client_weighting: Optional[tff.learning.ClientWeighting] = tff.learning.ClientWeighting.NUM_EXAMPLES,
    model_distributor: Optional[tff.learning.templates.DistributionProcess] = None,
    model_aggregator: Optional[tff.aggregators.WeightedAggregationFactory] = None,
    metrics_aggregator: Optional[tff.learning.metrics.MetricsAggregatorType] = None,
    loop_implementation: tff.learning.LoopImplementation = tff.learning.LoopImplementation.DATASET_REDUCE
) -> tff.learning.templates.LearningProcess

Used in the notebooks

Used in the tutorials
Federated Learning for Image Classification Federated Learning for Text Generation High-performance simulations with TFF TFF simulations with accelerators Working with tff's ClientData.

This function creates a tff.learning.templates.LearningProcess that performs federated averaging on client models. The iterative process has the following methods inherited from tff.learning.templates.LearningProcess:

initialize: A tff.Computation with the functional type signature ( -> S@SERVER), where S is a tff.learning.templates.LearningAlgorithmState representing the initial state of the server.
next: A tff.Computation with the functional type signature (<S@SERVER, {B*}@CLIENTS> -> <L@SERVER>) where S is a tff.learning.templates.LearningAlgorithmState whose type matches the output of initialize and {B*}@CLIENTS represents the client datasets. The output L contains the updated server state, as well as aggregated metrics at the server, including client training metrics and any other metrics from distribution and aggregation processes.
get_model_weights: A tff.Computation with type signature (S -> M), where S is a tff.learning.templates.LearningAlgorithmState whose type matches the output of initialize and next, and M represents the type of the model weights used during training.
set_model_weights: A tff.Computation with type signature (<S, M> -> S), where S is a tff.learning.templates.LearningAlgorithmState whose type matches the output of initialize and M represents the type of the model weights used during training.

Each time the next method is called, the server model is communicated to each client using the provided model_distributor. For each client, local training is performed using client_optimizer_fn. Each client computes the difference between the client model after training and its initial model. These model deltas are then aggregated at the server using a weighted aggregation function, according to client_weighting. The aggregate model delta is applied at the server using a server optimizer.

Args
`model_fn`	A no-arg function that returns a `tff.learning.models.VariableModel`, 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.
`client_optimizer_fn`	A `tff.learning.optimizers.Optimizer`.
`server_optimizer_fn`	A `tff.learning.optimizers.Optimizer`. By default, this uses `tff.leanring.optimizers.build_sgdm` with a learning rate of 1.0.
`client_weighting`	A member of `tff.learning.ClientWeighting` that specifies a built-in weighting method. By default, weighting by number of examples is used.
`model_distributor`	An optional `DistributionProcess` that distributes the model weights on the server to the clients. If set to `None`, the distributor is constructed via `tff.learning.templates.build_broadcast_process`.
`model_aggregator`	An optional `tff.aggregators.WeightedAggregationFactory` used to aggregate client updates on the server. If `None`, this is set to `tff.aggregators.MeanFactory`.
`metrics_aggregator`	A function that takes in the metric finalizers (i.e., `tff.learning.models.VariableModel.metric_finalizers()`) and a `tff.types.StructWithPythonType` of the unfinalized metrics (i.e., the TFF type of `tff.learning.models.VariableModel.report_local_unfinalized_metrics()`), and 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.

Returns
A `tff.learning.templates.LearningProcess`.

Raises
`TypeError`	If arguments are not the documented types.

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Used in the notebooks

Args

Returns

Raises

tff.learning.algorithms.build_weighted_fed_avg