Tensorflow-federated

Major Features and Improvements

* Python 2 support is now deprecated and will be removed in a future release.
* `federated_map` now works with both `tff.SERVER` and `tff.CLIENT`
placements.
* `federated_zip` received significant performance improvements and now works
recursively.
* Added retry logic to gRPC calls in the execution stack.

Breaking Changes

* `collections.OrderedDict` is now required in many places rather than
standard Python dictionaries.

Bug Fixes

* Fixed computation of the number of examples when Keras is using multiple
inputs.
* Fixed an assumption that `tff.framework.Tuple` is returned from
`IterativeProcess.next`.
* Fixed argument packing in polymorphic invocations on the new executor API.
* Fixed support for `dir()` in `ValueImpl`.
* Fixed a number of issues in the Colab / Jupyter notebook tutorials.

Bug Fixes

* Updated to use `grpcio` `1.24.3`.

Major Features and Improvements

* Add a `federated_sample` aggregation that is used to collect a sample of
client values on the server using reservoir sampling.
* Updated to use `tensorflow` `2.0.0` and `tensorflow-addons` `0.6.0` instead
of the coorisponding nightly package in the `setup.py` for releasing TFF
Python packages.
* Updated to use `tensorflow-privacy` `0.2.0`.
* Added support for `attr.s` classes type annotations.
* Updated streaming `Execute` method on `tff.framework.ExecutorService` to be
asynchronous.
* PY2 and PY3 compatibility.

Major Features and Improvements

* TFF is now fully compatible and dependent on TensorFlow 2.0
* Add stateful aggregation with differential privacy using TensorFlow Privacy
(https://pypi.org/project/tensorflow-privacy/).
* Additional stateful aggregation lwith compression using TensorFlow Model
Optimization (https://pypi.org/project/tensorflow-model-optimization/).
* Improved executor stack for simulations, documentation and scripts for
starting simulations on GCP.
* New libraries for creating synthetic IID and non-IID datsets in simulation.

Breaking Changes

* `examples` package split to `simulation` and `research`.

Bug Fixes

* Various error message string improvements.
* Dataset serialization fixed for V1/V2 datasets.
* `tff.federated_aggregate` supports `accumulate`, `merge` and `report`
methods with signatures containing tensors with undefined dimensions.

Major Features and Improvements

* Improvements in the executor stack: caching, deduplication, bi-directional
streaming mode, ability to specify physical devices.
* Components for integration with custom mapreduce backends
(`tff.backends.mapreduce`).
* Improvements in simulation dataset APIs: `ConcreteClientData`, random seeds,
stack overflow dataset, updated documentation.
* Utilities for encoding and various flavors of aggregation.

Breaking Changes

* Removed support for the deprecated `tf.data.Dataset` string iterator handle.
* Bumps the required versions of grpcio and tf-nightly.

Bug Fixes

* Fixes in notebooks, typos, etc.
* Assorted fixes to align with TF 2.0.
* Fixes thread cleanup on process exit in the high-performance executor.

Thanks to our Contributors

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

Gui-U, Krishna Pillutla, Sergii Khomenko.

Major Features and Improvements

* High-performance simulation components and tutorials.

Breaking Changes

* Refactoring/consolidation in utility functions in tff.framework.
* Switches some of the tutorials to new PY3-only executor stack components.

Bug Fixes

* Includes the `examples` directory in the pip package.
* Pip installs for TensorFlow and TFF in tutorials.
* Patches for asyncio in tutorials for use in Jupyter notebooks.
* Python 3 compatibility issues.
* Support for `federated_map_all_equal` in the reference executor.
* Adds missing implementations of generic constants and operator intrinsics.
* Fixes missed link in compatibility section of readme.
* Adds some of the missing intrinsic reductions.

Thanks to our Contributors

This release contains contributions from many people at Google.