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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
.