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Module: tff.learning

Libraries for building federated learning algorithms.

Currently, tff.learning provides a few types of functionality.

The library also contains classes of models that are used for the purposes of model training. See tff.learning.Model for the overall base class, and tff.learning.models for related model classes.


algorithms module: Libraries providing implementations of federated learning algorithms.

framework module: Libraries for developing federated learning algorithms.

metrics module: Libraries for working with metrics in federated learning algorithms.

models module: Libraries for working with models in federated learning algorithms.

optimizers module: Libraries for optimization algorithms.

reconstruction module: Libraries for using federated reconstruction algorithms.

templates module: Libraries of specialized processes used for building learning algorithms.


class BatchOutput: A structure that holds the output of a tff.learning.Model.

class ClientFedAvg: Client TensorFlow logic for Federated Averaging.

class ClientWeighting: Enum for built-in methods for weighing clients.

class Model: Represents a model for use in TensorFlow Federated.

class ModelWeights: A container for the trainable and non-trainable variables of a Model.


add_debug_measurements(...): Adds measurements suitable for debugging learning processes.

build_federated_averaging_process(...): Builds an iterative process that performs federated averaging.

build_federated_evaluation(...): Builds the TFF computation for federated evaluation of the given model.

build_local_evaluation(...): Builds the local TFF computation for evaluation of the given model.

build_personalization_eval(...): Builds the TFF computation for evaluating personalization strategies.

compression_aggregator(...): Creates aggregator with compression and adaptive zeroing and clipping.

dp_aggregator(...): Creates aggregator with adaptive zeroing and differential privacy.

federated_aggregate_keras_metric(...): Aggregates variables a keras metric placed at CLIENTS to SERVER.


robust_aggregator(...): Creates aggregator for mean with adaptive zeroing and clipping.

secure_aggregator(...): Creates secure aggregator with adaptive zeroing and clipping.

state_with_new_model_weights(...): Returns a ServerState with updated model weights.