View source on GitHub
|
Datasets for running TensorFlow Federated simulations.
Modules
celeba module: Libraries for the federated CelebA dataset for simulation.
cifar100 module: Libraries for the federated CIFAR-100 dataset for simulation.
emnist module: Libraries for the federated EMNIST dataset for simulation.
flair module: Libraries for loading the FLAIR dataset.
gldv2 module: Libraries for the federated Google Landmark v2 dataset for simulation.
inaturalist module: Libraries for the federated iNaturalist dataset for simulation.
shakespeare module: Libraries for the Shakespeare dataset for federated learning simulation.
stackoverflow module: Libraries for the Stackoverflow dataset for federated learning simulation.
Classes
class ClientData: Object to hold a federated dataset.
class FilePerUserClientData: A tff.simulation.datasets.ClientData that maps a set of files to a dataset.
class SqlClientData: A tff.simulation.datasets.ClientData backed by an SQL file.
class TestClientData: A tff.simulation.datasets.ClientData intended for test purposes.
class TransformingClientData: Transforms client data, potentially expanding by adding pseudo-clients.
Functions
build_dataset_mixture(...): Build a new dataset that probabilistically returns examples.
build_single_label_dataset(...): Build a new dataset that only yields examples with a particular label.
build_synthethic_iid_datasets(...): Constructs an iterable of IID clients from a tff.simulation.datasets.ClientData.
load_and_parse_sql_client_data(...): Load a ClientData arises by parsing a serialized SqlClientData.
save_to_sql_client_data(...): Serialize a federated dataset into a SQL database compatible with SqlClientData.