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Libraries for developing Federated Learning algorithms.
Classes
class ClientDeltaFn: Represents a client computation that produces an update to a model.
class ClientOutput: Structure for outputs returned from clients during federated optimization.
class EnhancedModel: A wrapper around a Model that adds sanity checking and metadata helpers.
class ModelWeights: A container for the trainable and non-trainable variables of a Model.
class ServerState: Represents the state of the server carried between rounds.
Functions
build_encoded_broadcast_process_from_model(...): Builds MeasuredProcess for weights of model returned by model_fn.
build_encoded_mean_process_from_model(...): Builds MeasuredProcess for weights of model returned by model_fn.
build_encoded_sum_process_from_model(...): Builds MeasuredProcess for weights of model returned by model_fn.
build_model_delta_optimizer_process(...): Constructs tff.templates.IterativeProcess for Federated Averaging or SGD.
build_stateless_broadcaster(...): Builds a MeasuredProcess that wraps tff.federated_broadcast.
build_stateless_mean(...): Builds a MeasuredProcess that wrapstff.federated_mean.
enhance(...): Wraps a tff.learning.Model as an EnhancedModel.
parameter_count_from_model(...): Computes count of trainable parameters for a model.
weights_type_from_model(...): Creates a tff.Type from a tff.learning.Model or callable that constructs a model.