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TensorFlow Federated 0.17.0

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@ZacharyGarrett ZacharyGarrett released this 27 Oct 17:38

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.

Breaking Changes

  • 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.

Bug Fixes

  • 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.

Known Bugs

Thanks to our Contributors

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

@amitport, @ronaldseoh