Release TensorFlow Federated 0.17.0 · google-parfait/tensorflow-federated

Major Features and Improvements

New tff.aggregators package with interfaces for stateful aggregation
compositions.
New Google Landmark Dataset tff.simulations.dataset.gldv2
New convenience APIs tff.type_clients and tff.type_at_server
Invert control of computation tracing methods to produce clearer Python
stack traces on error.
Move executor creation to a factory pattern in executor service, allowing
distributed runtimes to be agnostic to number of clients.
Significant improvements of type serialization/deserialization
New tff.simulations.compose_dataset_computation_with_iterative_process API
to move execution of client dataset construction to executor stack leaves.
Extend parameterization of tff.learning.build_federated_averaging_process
with use_experimental_simulation_loop argument to better utilize multi-GPU
setups.

Removed tff.utils.StatefulFn, replaced by tff.templates.MeasuredProcess.
Removed tff.learning.assign_weights_to_keras_model
Stop removing OptimizeDataset ops from tff.tf_computations.
The research/ directory has been moved to
http://github.com/google-research/federated.
Updates to input_spec argument for tff.learning.from_keras_model.
Updated TensorFlow dependency to 2.3.0.
Updated TensorFlow Model Optimization dependency to 0.4.0.

Fixed streaming mode hang in remote executor.
Wrap collections.namedtuple._asdict calls in collections.OrderedDict to
support Python 3.8.
Correctly serialize/deserialize tff.TensorType with unknown shapes.
Cleanup TF lookup HashTable resources in TFF execution.
Fix bug in Shakespeare dataset where OOV and last vocab character were the
same.
Fix TFF ingestion of Keras models with shared embeddings.
Closed hole in compilation to CanonicalForm.

"Federated Learning for Image Classification" tutorial fails to load
projector plugin for tensorboard (https://github.com/tensorflow/federated/issues/914).
Certain Keras models with activity regularization fail in execution with
unliftable error (https://github.com/tensorflow/federated/issues/913).

This release contains contributions from many people at Google, as well as: