tff.simulation.FromTensorSlicesClientData

ClientData based on tf.data.Dataset.from_tensor_slices.

Inherits From: ClientData

tff.simulation.FromTensorSlicesClientData(
    tensor_slices_dict
)

tensor_slices_dict A dictionary keyed by client_id, where values are structures suitable for passing to tf.data.Dataset.from_tensor_slices.

ValueError If a client with no data is found.

client_ids A list of string identifiers for clients in this dataset.
dataset_computation A tff.Computation accepting a client ID, returning a dataset.

ClientData implementations that don't support dataset_computation should raise NotImplementedError if this attribute is accessed.

element_type_structure The element type information of the client datasets.

elements returned by datasets in this ClientData object.

Methods

create_tf_dataset_for_client

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create_tf_dataset_for_client(
    client_id
)

Creates a new tf.data.Dataset containing the client training examples.

Args
client_id The string client_id for the desired client.

Returns
A tf.data.Dataset object.

create_tf_dataset_from_all_clients

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create_tf_dataset_from_all_clients(
    seed: Optional[int] = None
) -> tf.data.Dataset

Creates a new tf.data.Dataset containing all client examples.

This function is intended for use training centralized, non-distributed models (num_clients=1). This can be useful as a point of comparison against federated models.

Currently, the implementation produces a dataset that contains all examples from a single client in order, and so generally additional shuffling should be performed.

Args
seed Optional, a seed to determine the order in which clients are processed in the joined dataset. The seed can be any 32-bit unsigned integer or an array of such integers.

Returns
A tf.data.Dataset object.

datasets

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datasets(
    limit_count: Optional[int] = None,
    seed: Optional[int] = None
) -> Iterable[tf.data.Dataset]

Yields the tf.data.Dataset for each client in random order.

This function is intended for use building a static array of client data to be provided to the top-level federated computation.

Args
limit_count Optional, a maximum number of datasets to return.
seed Optional, a seed to determine the order in which clients are processed in the joined dataset. The seed can be any 32-bit unsigned integer or an array of such integers.

from_clients_and_fn

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@classmethod
from_clients_and_fn(
    client_ids: Iterable[str],
    create_tf_dataset_for_client_fn: Callable[[str], tf.data.Dataset]
) -> "ConcreteClientData"

Constructs a ClientData based on the given function.

Args
client_ids A non-empty list of client_ids which are valid inputs to the create_tf_dataset_for_client_fn.
create_tf_dataset_for_client_fn A function that takes a client_id from the above list, and returns a tf.data.Dataset.

Returns
A ClientData.

preprocess

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preprocess(
    preprocess_fn: Callable[[tf.data.Dataset], tf.data.Dataset]
) -> "PreprocessClientData"

Applies preprocess_fn to each client's data.

train_test_client_split

View source

@classmethod
train_test_client_split(
    client_data: "ClientData",
    num_test_clients: int
) -> Tuple['ClientData', 'ClientData']

Returns a pair of (train, test) ClientData.

This method partitions the clients of client_data into two ClientData objects with disjoint sets of ClientData.client_ids. All clients in the test ClientData are guaranteed to have non-empty datasets, but the training ClientData may have clients with no data.

Args
client_data The base ClientData to split.
num_test_clients How many clients to hold out for testing. This can be at most len(client_data.client_ids) - 1, since we don't want to produce empty ClientData.

Returns
A pair (train_client_data, test_client_data), where test_client_data has num_test_clients selected at random, subject to the constraint they each have at least 1 batch in their dataset.

Raises
ValueError If num_test_clients cannot be satistifed by client_data, or too many clients have empty datasets.