Skip to content

Latest commit

 

History

History
199 lines (132 loc) · 6.21 KB

RELEASE.md

File metadata and controls

199 lines (132 loc) · 6.21 KB

Release 0.10.1

Bug Fixes

  • Updated to use grpcio 1.24.3.

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.

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.

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.

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.

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.

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

Release 0.4.0

Major Features and Improvements

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

Release 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.

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.

Release 0.1.0

Initial public release.