Module: tff.learning

Libraries for building federated learning algorithms.

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

Algorithmic building blocks (see tff.learning.templates) for constructing federated learning algorithms. These are algorithms centered around the client work, server work, broadcast, or aggregation steps of a federated algorithms, and are intended to compose in a somewhat modular fashion.
End-to-end federated learning algorithms (such as tff.learning.algorithms.build_weighted_fed_avg) that combine broadcast, client work, aggregation, and server update logic into a single algorithm (often by composing the building blocks discussed above). This library also provides end-to-end algorithms for federated evaluation (see tff.learning.build_federated_evaluation).
Functionality supporting the development of the algorithms above. This includes tff.learning.optimizers, tff.learning.metrics and recommended aggregators, such as tff.learning.robust_aggregator.

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

Modules

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.

programs module: Package of methods for compositional federated program logic.

reconstruction module: Libraries for using federated reconstruction algorithms.

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

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

class Model: Represents a variable-based 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.

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

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.

ddp_secure_aggregator(...): Creates aggregator with adaptive zeroing and distributed DP.

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

entropy_compression_aggregator(...): Creates an aggregation factory for quantization and entropy coding.

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.