Tensorflow-federated

Bug Fixes

* Fixed issues in tutorial notebooks.

Major Features and Improvements

* Updated `absl-py` package dependency to `0.9.0`.
* Updated `h5py` package dependency to `2.8.0`.
* Updated `numpy` package dependency to `1.17.5`.
* Updated `tensorflow-privacy` package dependency to `0.2.2`.

Breaking Changes

* Deprecated `dummy_batch` parameter of the `tff.learning.from_keras_model`
function.

Bug Fixes

* Fixed issues with executor service using old executor API.
* Fixed issues with remote executor test using old executor API.
* Fixed issues in tutorial notebooks.

Major Features and Improvements

* Upgraded tensorflow dependency from `2.0.0` to `2.1.0`.
* Upgraded tensorflow-addons dependency from `0.6.0` to `0.7.0`.
* Upgraded attr dependency from `18.2` to `19.3`.
* Upgraded tfmot dependency from `0.1.3` to `0.2.1`.
* Added a federated partition of the CIFAR-100 dataset to
`tff.simulation.datasets.cifar100`.
* Made the high performance, parallel executor the default (replacing the
reference executor).
* Added a new `tff.learning.build_personalization_eval` for evaluating model
personalization strategies.
* Added new federated intrinsic `tff.federated_secure_sum`.
* `tff.learning.build_federated_averaing_process()` now takes a
`client_optimizer_fn` and a `tff.learning.Model`.
`tff.learning.TrainableModel` is now deprecated.
* Improved performance in the high performance executor stack.
* Implemented and exposed `tff.framework.ExecutorFactory`; all
`tff.framework...executor_factory` calls now return an instance of this
class.
* Added `remote_executor_example` binary which demonstrates using the
RemoteExecutor across multi-machine deployments.
* Added `close()` method to the Executor, allowing subclasses to proactively
release resources.
* Updated documentation and scripts for creating Docker images of the TFF
runtime.
* Automatically call `tff.federated_zip` on inputs to other federated
intrinsics.

Breaking Changes

* Dropped support for Python2.
* Renamed `tff.framework.create_local_executor` (and similar methods) to
`tff.framework.local_executor_factory`.
* Deprecated `federated_apply()`, instead use `federated_map()` for all
placements.

Bug Fixes

* Fixed problem with different instances of the same model having different
named types. `tff.learning.ModelWeights` no longer names the tuple fields
returned for model weights, instead relying on an ordered list.
* `tff.sequence_*` on unplaced types now correctly returns a `tff.Value`.

Known Bugs

* `tff.sequence_*`.. operations are not implemented yet on the new
high-performance executor stack.
* A subset of previously-allowed lambda captures are no longer supported on
the new execution stack.

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.