TensorFlow Federated 0.78.0

Release 0.78.0

Major Features and Improvements

• Moved aggregation from https://github.com/google-parfait/federated-compute
  to TFF to consolidate the federated language and remove circular
  dependencies.

Breaking Changes

• Updated rules_license to version 0.0.8.
• Removed elias_gamma_encode module.
• Removed tensorflow_compression dependency.

TensorFlow Federated 0.77.0

Release 0.77.0

Major Features and Improvements

• Added an implementation of __eq__() on building blocks.

Bug Fixes

• Fix #4588: Target Haswell CPU architectures (-march=haswell) instead of
  whatever is native to the build infrastructure to ensure that binaries in
  the pip package and executable on Colab CPU runtimes.

TensorFlow Federated 0.76.0

Release 0.76.0

Major Features and Improvements

• Added a Literal to the TFF language, part 2. This change updates the
  tracing and execution portions of TFF to begin using the Literal.
• Added an implementation of the Adafactor optimizer to
  tff.learning.optimizers.build_adafactor
• Added a new field, content, to the Data proto.

Breaking Changes

• Removed the check_foo() methods on building blocks.
• Removed tff.data, this symbol is not used.

Bug Fixes

• Fix a bug where the pip package default executor stack cannot execute
  computations that have Lambdas under sequence_* intrinsics.

TensorFlow Federated 0.75.0

Release 0.75.0

Major Features and Improvements

• Updated the type annotation for MaterializedValue to include the Python
  scalar types in addition to the numpy scalar types.
• Added a Literal to the TFF language, part 1.
• Added Literal to the framework package.
• Extended
  tff.learning.algorithms.build_weighted_fed_avg_with_optimizer_schedule to
  support tff.learning.models.FunctionalModel.

Breaking Changes

• Deleted the tff.learning.framework namespace⚰️.

Bug Fixes

• Fixed logic for determining if a value can be cast to a specific dtype.
• Fixed a bug where repeated calls to
  FilePerUserClientData.create_tf_dataset_for_client could blow up memory
  usage

TensorFlow Federated 0.74.0

Release 0.74.0

Major Features and Improvements

• Make some of the C++ executor APIs public visibility for downstream repos.
• Moved the DataType protobuf object into its own module. Moving the
  DataType object into its own module allows DataType to be used outside
  of a Computation more easily and prevents a circular dependency between
  Computation and Array which both require a DataType.
• Updated build_apply_optimizer_finalizer to allow custom reject update
  function.
• Relaxed the type requirement of the attributes of ModelWeights to allow
  assigning list or tuples of matching values to other sequence types on
  tf.keras.Model instances.
• Improved the errors raised by JAX computations for various types.
• Updated tutorials to use recommended tff.learning APIs.

Breaking Changes

• Removed the runtime-agnostic support for tf.RaggedTensor and
  tf.SparseTensor.

Full Changelog: v0.73.0...v0.74.0

TensorFlow Federated 0.73.0

Release 0.73.0

Major Features and Improvements

• Make some of the C++ executor APIs public visibility for downstream repos.
• tff.learning.algorithms.build_fed_kmeans supports floating point weights,
  enabling compatibility with tff.aggregators using differential privacy.
• Added two new metrics aggregators:
  tff.learning.metrics.finalize_then_sample and
  tff.learning.metrics.FinalizeThenSampleFactory.

Breaking Changes

• Remove the ability to return SequenceType from tff.federated_computation
  decorated callables.

Bug Fixes

• tff.learning algorithms now correctly do not include metrics for clients
  that had zero weight due to model updates containing non-finite values.
  Previously the update was rejected, but the metrics still aggregated.

TensorFlow Federated 0.72.0

Release 0.72.0

Major Features and Improvements

• Added an async XLA runtime under tff.backends.xla.

Breaking Changes

• Updated tensorflow-privacy version to 0.9.0.
• Removed the deprecated type_signature parameter from the tff.program.ReleaseManager.release method.

TensorFlow Federated 0.71.0

Release 0.71.0

Major Features and Improvements

• Added new environment-specific packages to TFF.

TensorFlow Federated 0.70.0

Breaking Changes

• Temporarily disable tff.program.PrefetchingDataSource due to flakiness
  from a lack of determinism.
• Removed support for invoking infer_type with TensorFlow values.
• Removed deprecated tff.aggregators.federated_(min|max)symbols, please use
  tff.federated_(min|max) instead.
• Removed support for creating a tff.TensorType using a tf.dtypes.DType.
• Removed tff.check_return_type.

Bug Fixes

• Declared OwnedValueId::INVALID_ID as a static constexpr.

TensorFlow Federated 0.69.0

Release 0.69.0

Major Features and Improvements

• The local_unfinalized_metrics_type argument to
  tff.learning.metrics.(secure_)sum_then_finalize is now optional (and is not
  actually used). It will be removed in a future release.

Breaking Changes

• tff.learning.metrics.(secure_)sum_then_finalize now return polymorphic
  computations. They can still be passed into algorithm builders (e.g.
  tff.learning.algorithms.build_weighted_fed_avg) but to be called directly
  they must first be traced with explicit types.
• Removed support for handling tf.TensorSpec using to_type, use
  tensorflow_to_type instead.
• Removed support for calling tff.TensorType using a tf.dtypes.DType.