tff.learning.build_federated_averaging_process

See Stable See Nightly

View source on GitHub

Builds an iterative process that performs federated averaging.

tff.learning.build_federated_averaging_process(
    model_fn: Callable[[], tff.learning.Model],
    client_optimizer_fn: Callable[[], tf.keras.optimizers.Optimizer],
    server_optimizer_fn: Callable[[], tf.keras.optimizers.Optimizer] = DEFAULT_SERVER_OPTIMIZER_FN,
    client_weight_fn: Callable[[Any], tf.Tensor] = None,
    stateful_delta_aggregate_fn: Optional[tff.utils.StatefulAggregateFn] = None,
    stateful_model_broadcast_fn: Optional[tff.utils.StatefulBroadcastFn] = None,
    *,
    broadcast_process: Optional[tff.templates.MeasuredProcess] = None,
    aggregation_process: Optional[tff.templates.MeasuredProcess] = None
) -> tff.templates.IterativeProcess

Used in the notebooks

Used in the tutorials
Federated Learning for Image Classification TFF for Federated Learning Research: Model and Update Compression TensorFlow Federated for Image Classification Federated Learning for Text Generation High-performance Simulation with Kubernetes

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

• initialize: A tff.Computation with the functional type signature ( -> S@SERVER), where S is a tff.learning.framework.ServerState representing the initial state of the server.
• next: A tff.Computation with the functional type signature (<S@SERVER, {B*}@CLIENTS> -> <S@SERVER, T@SERVER>) where S is a tff.learning.framework.ServerState whose type matches that of the output of initialize, and {B*}@CLIENTS represents the client datasets, where B is the type of a single batch. This computation returns a tff.learning.framework.ServerState representing the updated server state and training metrics that are the result of tff.learning.Model.federated_output_computation during client training.

Each time the next method is called, the server model is broadcast to each client using a broadcast function. For each client, one epoch of local training is performed via the tf.keras.optimizers.Optimizer.apply_gradients method of the client optimizer. Each client computes the difference between the client model after training and the initial broadcast model. These model deltas are then aggregated at the server using some aggregation function. The aggregate model delta is applied at the server by using the tf.keras.optimizers.Optimizer.apply_gradients method of the server optimizer.

Args
model_fn	A no-arg function that returns a tff.learning.Model. 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.
client_optimizer_fn	A no-arg callable that returns a tf.keras.Optimizer.
server_optimizer_fn	A no-arg callable that returns a tf.keras.Optimizer. By default, this uses tf.keras.optimizers.SGD with a learning rate of 1.0.
client_weight_fn	Optional function that takes the output of model.report_local_outputs and returns a tensor providing the weight in the federated average of model deltas. If not provided, the default is the total number of examples processed on device.
stateful_delta_aggregate_fn	A tff.utils.StatefulAggregateFn where the next_fn performs a federated aggregation and upates state. It must have TFF type (<state@SERVER, value@CLIENTS, weights@CLIENTS> -> <state@SERVER, aggregate@SERVER>), where the value type is tff.learning.framework.ModelWeights.trainable corresponding to the object returned by model_fn. By default performs arithmetic mean aggregation, weighted by client_weight_fn. Must be None if aggregation_process is not None.
stateful_model_broadcast_fn	A tff.utils.StatefulBroadcastFn where the next_fn performs a federated broadcast and upates state. It must have TFF type (<state@SERVER, value@SERVER> -> <state@SERVER, value@CLIENTS>), where the value type is tff.learning.framework.ModelWeights corresponding to the object returned by model_fn. The default is the identity broadcast. Must be None if broadcast_process is not None.
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). Must be None if stateful_model_broadcast_fn is not None.
aggregation_process	a tff.templates.MeasuredProcess that aggregates the model updates on the clients back to the server. It must support the signature ({input_values}@CLIENTS-> output_values@SERVER). Must be None if stateful_delta_aggregate_fn is not None.

Returns
A tff.templates.IterativeProcess.