Tensorflow-federated

Major Features and Improvements

* Reduced memory usage for entropy compression.
* Updated `com_google_protobuf` version to `v3.19.0`.
* Removed dependency on `six`.

Breaking Changes

* Removed default value for the key parameter from the abstract base class
`tff.program.ReleaseManager`.

Bug Fixes

* Fixed a whitespace syntax issue with shutting down a process when using the
localhost C++ execution context.
* Modified `tff.simulation.build_uniform_sampling_fn` so that the output
raises on non-integer inputs.
* Only wait a subprocess instance if it is not None.

Major Features and Improvements

* TFF-C++ runtime now installed by default. Note that this release will have a
significantly larger PIP package size.
* Introduce `tff.learning.programs` for federated program-logic using the
`tff.program` APIs.
* Updated `tensorflow` to version `2.11.0`.
* Updated `tensorflow_compression` to version `2.11.0`.
* Updated `bazel_skylib` to version `1.3.0`.

Major Features and Improvements

* Skip model updates that are non-finite in
`tff.learning.templates.build_apply_optimizer_finalizer`.

Breaking Changes

* Removed deprecated APIs in `tff.learning.framework`
* Update the Python package scripts to use Python 3.10 by default.
* Remove module wildcard imports from **init**.py files in TFF.
* Update the Python package scripts to use Python 3.10 by default.

Bug Fixes

* Remove `functools.wraps` within `tff.tf_computation`.
* Fix typo in iNaturalist dataset docstring.

Major Features and Improvements

* Added `tff.learning.models.FunctionModel` support to all methods in
`tff.learning.algorithms`.
* Added support for `tf.data.DataSpec` to `tff.types.infer_unplaced_type`.
* Use a `tensorflow::ThreadPool` for parallelism inside the C++
`TensorFlowExecutor`.
* Introduced a new `tff.experimental_tf_fn_computation` tracing decorator that
uses `FunctionDef` instead of `GraphDef` tracing, providing `tf.function`
auto-control-dependencies.
* Renamed `number_of_clients` to `num_clients` in the federated program API.
* Replaced the following API with composers API in `tff.learning.templates`.
* `tff.learning.framework.build_model_delta_optimizer_process`
* `tff.learning.framework.ClientDeltaFn`

Bug Fixes

* Fixed a bug in the “Client-efficient large-model federated learning”
tutorial to use the correct dense shape.

Major Features and Improvements

* Added `tff.learning.models.FunctionalModel` support to
`tff.learning.algorithms.build_mime_lite`.
* Updated `tensorflow-privacy` to version `0.8.6`.
* Added an abstract interface describing an asynchronous context
* Removed references to `tff.framework.Context`.
* Added `tff.simulation.datasets.gldv2.get_synthetic`.
* Added prefetching data source in `tff.program.PrefetchingDataSource`.

Breaking Changes

* Deleted deprecated
`tff.learning.framework.build_encoded_broadcast_process_from_model`.
* Deprecated `tff.learning.ModelWeights` and alias
`tff.learning.framework.ModelWeights`, has now moved to
`tff.learning.models.ModelWeights`. Code should be updated before the next
release.

Bug Fixes

* Fixed a bug with variable creation order of metrics in
`tff.learning.models.functional_model_from_keras`.
* Improved `tff.tf_computation` tracing to also trace `functools.partial`
objects.

Known Bugs

* Colab compatibility: TFF requires Python 3.9 while Colab runtime uses Python
3.7.

Major Features and Improvements

* Added support for Python 3.10.
* Improved support for `numpy` values in the `tff.program` API.
* Increased dataset serialization size limit to 100MB.
* Added a new method `tff.learning.ModelWeights.convert_variables_to_arrays`.
* Added new metrics aggregation factories under `tff.learning.metrics`.
* Parallelized aggregation in `tff.framework.ComposingExecutorFactory`.

Breaking Changes

* Updated to use `jax` and `jaxlib` version `0.3.14`.
* Renamed `tff.program.CoroValueReference` to
`tff.program.AwaitableValueReference` to reflect the relaxed contract.

Bug Fixes

* Improved documentation for `tff.simulation.build_uniform_sampling_fn`,
`tff.learning.robust_aggregator`,
`tff.aggregators.PrivateQuantileEstimationProcess`.
* Fixed documentation bug for tutorial “High-performance Simulation with
Kubernetes”.
* Fixed bug where momentum hyperparameters were added to SGDM optimizer when
momentum was set to 0.
* Removed assertion that preprocessed datasets in a
`tff.simulation.baselines.BaselineTask` have the same element structure.
* Fixed a memory leak when moving numpy arrays across the Python and C++
boundary in the C++ executor.
* Fixed bug in the federated program API when using multiple release managers
to release the same value.

Thanks to our Contributors

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