Tensorflow-federated

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 turorials.
* 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.

Major Features and Improvements

* Support for multiple outputs and loss functions in `keras` models.
* Support for stateful broadcast and aggregation functions in federated
averaging and federated SGD APIs.
* `tff.utils.update_state` extended to handle more general `state` arguments.
* Addition of `tff.utils.federated_min` and `tff.utils.federated_max`.
* Shuffle `client_ids` in `create_tf_dataset_from_all_clients` by default to
aid optimization.

Breaking Changes

* Dependencies added to `requirements.txt`; in particular, `grpcio` and
`portpicker`.

Bug Fixes

* Removes dependency on `tf.data.experimental.NestedStructure`.

Thanks to our Contributors

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

Dheeraj R Reddy, Squadrick.

Major Features and Improvements

* Removed source level TF dependencies and switched from `tensorflow` to
`tf-nightly` dependency.
* Add support for `attr` module in TFF type system.
* Introduced new `tff.framework` interface layer.
* New AST transformations and optimizations.
* Preserve Python container usage in `tff.tf_computation`.

Bug Fixes

* Updated TFF model to reflect Keras `tf.keras.model.weights` order.
* Keras model with multiple inputs. 416

Major Features and Improvements

* New `tff.simulation.TransformingClientData` API and associated infinite
EMNIST dataset (see http://tensorflow.org/federated/api_docs/python/tff for
details)

Breaking Change

* Normalized `func` to `fn` across the repository (rename some parameters and
functions)

Bug Fixes

* Wrapped Keras models can now be used with
`tff.learning.build_federated_evaluation`
* Keras models with non-trainable variables in intermediate layers (e.g.
BatchNormalization) can be assigned back to Keras models with
`tff.learning.ModelWeights.assign_weights_to`