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Libraries for running Federated Learning simulations.
Modules
datasets module: Datasets for running Federated Learning simulations.
models module: Models for running Federated Learning simulations.
Classes
class CSVMetricsManager: Utility class for saving/loading experiment metrics via a CSV file.
class ClientData: Object to hold a federated dataset.
class FileCheckpointManager: A checkpoint manager backed by a file system.
class FilePerUserClientData: A tf.simulation.ClientData that maps a set of files to a dataset.
class FromTensorSlicesClientData: ClientData based on tf.data.Dataset.from_tensor_slices.
class HDF5ClientData: A tff.simulation.ClientData backed by an HDF5 file.
class MetricsManager: An abstract base class for metrics managers.
class TensorBoardManager: Utility class for saving metrics using tf.summary.
class TransformingClientData: Transforms client data, potentially expanding by adding pseudo-clients.
Functions
build_uniform_client_sampling_fn(...): Builds a function that (pseudo-)randomly samples clients.
compose_dataset_computation_with_computation(...): Builds a new tff.Computation which constructs datasets on clients.
compose_dataset_computation_with_iterative_process(...): Builds a new iterative process which constructs datasets on clients.
run_server(...): Runs a gRPC server hosting a simulation component in this process.
server_context(...): Context manager yielding gRPC server hosting simulation component.