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Release 0.14.0

Major Features and Improvements

  • Multiple TFF execution speedups.
  • New tff.templates.MeasuredProcess specialization of IterativeProcess.
  • Increased optimization of the tff.templates.IterativeProcess -> tff.backends.mapreduce.CanonicalForm compiler.

Breaking Changes

  • Moved tff.utils.IterativeProcess to tff.templates.IterativeProcess.
  • Removed tff.learning.TrainableModel, client optimizers are now arguments on the tff.learning.build_federated_averaging_process.
  • Bump required version of pip packages for tensorflow (2.2), numpy (1.18), pandas (0.24), grpcio (1.29).

Bug Fixes

  • Issue with GPUs in multimachine simulations not being utilized, and bug on deserializing datasets with GPU-backed runtime.
  • TensorFlow lookup table initialization failures.

Known Bugs

  • In some situations, TF will attempt to push Datasets inside of tf.functions over GPU device boundaries, which fails by default. This can be hit by certain usages of TFF, e.g. as tracked here.

Thanks to our Contributors

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

jvmcns@

Release 0.13.1

Bug Fixes

  • Fixed issues in tutorial notebooks.

Release 0.13.0

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.

Release 0.12.0

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.

Release 0.11.0

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.

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.