TensorFlow Federated 0.10.1

Release 0.10.1

Bug Fixes

• Updated to use grpcio 1.24.3.

TensorFlow Federated 0.10.0

Release 0.10.0

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 compatability.

TensorFlow Federated 0.9.0

Release 0.9.0

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.

TensorFlow Federated 0.8.0

Release 0.8.0

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.

TensorFlow Federated 0.7.0

Release 0.7.0

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.

TensorFlow Federated 0.6.0

Release 0.6.0

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.

TensorFlow Federated 0.5.0

Release 0.5.0

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

TensorFlow Federated 0.4.0

Release 0.4.0

Major Features and Improvements

• New tff.simulation.TransformingClientData API and associated inifinite EMNIST dataset (see 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

TensorFlow Federated 0.3.0

Breaking Changes

• Rename tff.learning.federated_average to tff.learning.federated_mean.
• Rename func arguments to fn throughout the API.

Bug Fixes

• Assorted fixes to typos in documentation and setup scripts.

TensorFlow Federated 0.2.0

Release 0.2.0

Major Features and Improvements

• Updated to use TensorFlow version 1.13.1.
• Implemented Federated SGD in tff.learning.build_federated_sgd_process().

Breaking Changes

• next() function of tff.utils.IteratedProcesss returned by build_federated_*_process() no longer unwraps single value tuples (always returns a tuple).

Bug Fixes

• Modify setup.py to require TensorFlow 1.x and not upgrade to 2.0 alpha.
• Stop unpacking single value tuples in next() function of objects returned by build_federated_*_process().
• Clear cached Keras sessions when wrapping Keras models to avoid referencing stale graphs.