Decorates/wraps Python functions as TFF federated/composite computations.

Used in the notebooks

Used in the tutorials

The term federated computation as used here refers to any computation that uses TFF programming abstractions. Examples of such computations may include federated training or federated evaluation that involve both client-side and server-side logic and involve network communication. However, this decorator/wrapper can also be used to construct composite computations that only involve local processing on a client or on a server.

The main feature that distinguishes federated computation function bodies in Python from the bodies of TensorFlow defuns is that whereas in the latter, one slices and dices tf.Tensor instances using a variety of TensorFlow ops, in the former one slices and dices tff.Value instances using TFF operators.

The supported modes of usage are identical to those for tff.tf_computation.


@tff.federated_computation((tff.FunctionType(tf.int32, tf.int32), tf.int32))
def foo(f, x):
  return f(f(x))

The above defines foo as a function that takes a tuple consisting of an unary integer operator as the first element, and an integer as the second element, and returns the result of applying the unary operator to the integer twice. The body of foo does not contain federated communication operators, but we define it with tff.federated_computation as it can be used as building block in any section of TFF code (except inside sections of pure TensorFlow logic).

*args Either a Python function, or TFF type spec, or both (function first), or neither. See also tff.tf_computation for an extended documentation.

If invoked with a function as an argument, returns an instance of a TFF computation constructed based on this function. If called without one, as in the typical decorator style of usage, returns a callable that expects to be called with the function definition supplied as a parameter. See also tff.tf_computation for an extended documentation.