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The TensorFlow Federated library.
Modules
aggregators module: Libraries for constructing federated aggregation.
backends module: Backends for constructing, compiling, and executing computations.
experimental module: The experimental part of the TensorFlow Federated (TFF) library.
framework module: Libraries for extending the TensorFlow Federated core library.
learning module: Libraries for using Federated Learning algorithms.
simulation module: Libraries for running Federated Learning simulations.
structure module: Container for structures with named and/or unnamed fields.
templates module: Templates for commonly used computations.
test module: Libraries for testing TensorFlow Federated.
utils module: Libraries for using and developing Federated algorithms.
Classes
class Computation: An abstract interface for all classes that represent computations.
class FederatedType: An implementation of tff.Type representing federated types in TFF.
class FunctionType: An implementation of tff.Type representing functional types in TFF.
class SequenceType: An implementation of tff.Type representing types of sequences in TFF.
class StructType: An implementation of tff.Type representing structural types in TFF.
class StructWithPythonType: A representation of a structure paired with a Python container type.
class TensorType: An implementation of tff.Type representing types of tensors in TFF.
class Type: An abstract interface for all classes that represent TFF types.
class TypedObject: An abstract interface for things that possess TFF type signatures.
class Value: An abstract base class for all values in the bodies of TFF computations.
Functions
check_returns_type(...): Checks that the decorated function returns values of the provided type.
federated_aggregate(...): Aggregates value from tff.CLIENTS to tff.SERVER.
federated_apply(...): Applies a given function to a federated value on tff.SERVER.
federated_broadcast(...): Broadcasts a federated value from the tff.SERVER to the tff.CLIENTS.
federated_collect(...): Returns a federated value from tff.CLIENTS as a tff.SERVER sequence.
federated_computation(...): Decorates/wraps Python functions as TFF federated/composite computations.
federated_eval(...): Evaluates a federated computation at placement, returning the result.
federated_map(...): Maps a federated value pointwise using a mapping function.
federated_mean(...): Computes a tff.SERVER mean of value placed on tff.CLIENTS.
federated_reduce(...): Reduces value from tff.CLIENTS to tff.SERVER using a reduction op.
federated_secure_sum(...): Computes a sum at tff.SERVER of a value placed on the tff.CLIENTS.
federated_sum(...): Computes a sum at tff.SERVER of a value placed on the tff.CLIENTS.
federated_value(...): Returns a federated value at placement, with value as the constituent.
federated_zip(...): Converts an N-tuple of federated values into a federated N-tuple value.
sequence_map(...): Maps a TFF sequence value pointwise using a given function mapping_fn.
sequence_reduce(...): Reduces a TFF sequence value given a zero and reduction operator op.
sequence_sum(...): Computes a sum of elements in a sequence.
tf_computation(...): Decorates/wraps Python functions and defuns as TFF TensorFlow computations.
to_type(...): Converts the argument into an instance of tff.Type.
to_value(...): Converts the argument into an instance of the abstract class tff.Value.
type_at_clients(...): Constructs a federated type of the form {T}@CLIENTS.
type_at_server(...): Constructs a federated type of the form T@SERVER.