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The TensorFlow Federated library.
Modules
aggregators module: Libraries for constructing federated aggregation.
analytics module: Libraries for using Federated Analytics algorithms.
backends module: Backends for constructing, compiling, and executing computations.
framework module: Libraries for extending the TensorFlow Federated core library.
jax module: Libraries for interacting with a JAX frontend and XLA backend.
learning module: Libraries for building federated learning algorithms.
profiler module: Utility functions for instrumenting code with timing and tracing data.
program module: Libraries for creating federated programs.
simulation module: Libraries for running TensorFlow Federated simulations.
structure module: Container for structures with named and/or unnamed fields.
templates module: Templates for commonly used computations.
tensorflow module: Libraries for interacting with a TensorFlow frontend and backend.
test module: Libraries for testing TensorFlow Federated.
types module: Libraries for interacting with the type of a computation.
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: A generic base class for values that appear in TFF computations.
Functions
federated_aggregate(...): Aggregates value from tff.CLIENTS to tff.SERVER.
federated_broadcast(...): Broadcasts a federated value from the tff.SERVER to the tff.CLIENTS.
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_max(...): Computes a max at tff.SERVER of a value placed on the tff.CLIENTS.
federated_mean(...): Computes a tff.SERVER mean of value placed on tff.CLIENTS.
federated_min(...): Computes a min at tff.SERVER of a value placed on the tff.CLIENTS.
federated_secure_select(...): Sends privately-selected values from a server database to clients.
federated_secure_sum(...): Computes a sum at tff.SERVER of a value placed on the tff.CLIENTS.
federated_secure_sum_bitwidth(...): Computes a sum at tff.SERVER of a value placed on the tff.CLIENTS.
federated_select(...): Sends selected values from a server database to 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.
jax_computation(...): Decorates/wraps Python functions containing JAX code as TFF computations.
sequence_map(...): Maps a TFF sequence value pointwise using a given function 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.
Other Members | |
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| CLIENTS |
Instance of tff.framework.PlacementLiteral
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| SERVER |
Instance of tff.framework.PlacementLiteral
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| version |
'0.83.0'
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