Tensorflow-federated

Major Features and Improvements

* DTensor TF executor is now integrated with the default TFF C++ worker.
* Added federated program documentation and guidelines.
* Removed the `pytype` dependency from TFF.
* `tff.learning.algorithms.build_fed_recon_eval` now supports TFF optimizers.

Breaking Changes

* Updated `tff.types.deserialize_type` to not accept/return `None`.
* Removed the `tff.framework.ComputationBuildingBlock.is_foo` methods.
* Renamed `tff.learning.algorithms.build_personalization_eval` to
`tff.learning.algorithms.build_personalization_eval_computation`
* `tff.learning.models.ReconstructionModel.from_keras_model` will now check
that global and local variables are disjoint, raise ValueError if they are
not.

Bug Fixes

* Fixed `tff.learning.models.ReconstructionModel.has_only_global_variables`
(it was returning incorrect value).

Major Features and Improvements

* Removed compression for `worker_binary`.
* Allowed tensor and numpy float-like objects in optimizer hyperparameters.
* Improved API/filtering logic in `FilteringReleaseManager`.

Breaking Changes

* Renamed `build_personalization_eval` to
`build_personalization_eval_computation`.
* Updated `tff.to_type` implementation and type annotation to not
accept/return `None`.

Bug Fixes

* Fixed and documented pytype errors in the `program` package.
* Fixed bug in how `tff.program.NativeFederatedContext` handles arguments of
various types.

Major Features and Improvements

* Updated algorithms built from `tff.learning.models.FunctionalModel` to allow
nested outputs.
* Added the `PrefetchingDataSource` back to the `tff.program` API now that the
flakiness has been fixed.

Bug Fixes

* Changed return type of
`tff.simulation.compose_dataset_computation_with_learning_process` to be a
`tff.learning.templates.LearningProcess`.
* Fixed flaky tests in `prefetching_data_source_test`.
* Fixed type annotations and lint errors.
* Cleaned up error messages and typing information in
`tff.learning.optimizers`.

Major Features and Improvements

* Allow functional models to return a structure.

Breaking Changes

* Removed support for handling `attrs` as containers in the `tff.program` API.
* Migrated the `personalization_eval` module to the algorithms package.
* Deleted the `tff.learning.build_local_evaluation` API.
* Migrated `tff.learning.reconstruction` to the `tff.learning.algorithms`
package.
* Updated to `dm-tree` version `0.1.8`.
* Updated to `dp-accounting` version `0.4.1`.
* Updated to `tensorflow-privacy` version `0.8.9`.

Major Features and Improvements

* Added Vizier backed tuning program logic to `tff.learning`.
* Updated the `tff.learning.programs.EvaluationManager` to clean up states
after recording the evaluation is completed.

Breaking Changes

* Removed deprecated `tff.learning.framework.ServerState` symbol.

Major Features and Improvements

* Removed `nest_asyncio` dependency from tutorials.
* Added a new
aggregatorr`tff.aggregators.DifferentiallyPrivateFactory.tree_adaptive` for
combining DP-FTRL (https://arxiv.org/abs/2103.00039) and adaptive clipping
(https://arxiv.org/abs/1905.03871).
* Updated `tff.learning.programs.EvaluationManager` to set the evaluation
deadline from the start time.

Breaking Changes

* Python runtime deleted; C++ runtime covers all known use-cases.

Bug Fixes

* Fixed a bug attempting to push `tf.data.Dataset` iterator ops onto GPUs.