Release 0.20.0

Major Features and Improvements

• Added tff.program API; this API is still in active development but can be
  used to compose shared and platform specific: program logic, components, and
  privacy concepts to create federated programs.
• Added support for Python 3.9.
• Added CelebA and iNaturalist datasets to tff.simulation.datasets.
• Added tff.analytics API for federated analytics, including private heavy
  hitters algorithms.
• Added tff.learning.algorithms API, including TFF implementations of
  FedProx, FedAvg with learning rate scheduling, federated k-Means, and
  MimeLite.
• Added tff.learning.metrics API to support easy configuration of
  cross-client metrics aggregation via the new metrics_aggregator argument.
• Added metrics_aggregator argument to
  tff.learning.build_federated_averaging_process and
  tff.learning.build_federated_evaluation.
• Added report_local_unfinalized_metrics and metric_finalizers methods to
  tff.learning.Model and deprecated report_local_outputs and
  federated_output_computation.
• Added tff.learning.optimizers API for building purely functional
  optimizers and implementations of SGD, Adagrad, Rmsprop, Adam, Yogi,
• Added tff.learning.reconstruction API for building partially local
  federated learning algorithms, including Federated Reconstruction.
• Added tff.learning.templates API to support building learning algorithms
  in a modular fashion.
• Added tff.simulation.baselines API to support evaluating learning
  algorithms on a suite of representative tasks.
• Added tff.aggregators.DifferentiallyPrivateFactory.tree_aggregation to
  support the DP-FTRL algorithm.
• Added tff.aggrgators.SecureModularSumFactory
• Added tff.aggregators.DiscreteFourierTransformFactory and
  tff.aggregators.HadamardTransformFactory to support rotation-based
  aggregators.
• Added tff.aggregators.concat_factory for aggregating structures as a
  single tensor.
• Added tff.backends.native.create_mergeable_comp_execution_context,
  tff.backends.native.set_mergeable_comp_execution_context; these can be
  used with a distributed runtime to scale to tens of thousands of clients.
• Improved performance of many tff.simulation.datasets.ClientData
  subclasses.
• Added tff.simulation.datasets.ClientData.serializable_dataset_fn
  attribute, enabling dataset creation within TF/TFF computations.
• Added debug_measurements option to aggregators in tff.learning.
• Added support for unambiguous zero-client aggregations.
• Added support for Python dataclasses as function parameters and return
  values for TFF computations.
• Added automatic insertion of tff.federated_zip to invocation of
  user-defined TFF federated computations.
• Added utilities to tff.simulation.datasets for saving federated datasets
  to a SQL database compatible with tff.simulation.datasets.SqlClientData.
• Added tff.learning.models.FunctionalModel and
  tff.learning.models.functional_model_from_keras.
• Increased max flow of tensors. Tensors now flow here, there, and everywhere.
• Updated the Python dependencies:
• Updated absl-py to version 1.0.0.
• Updated attrs to version 21.2.0.
• Added farmhashpy version 0.4.0.
• Updated jax to version 0.2.27.
• Updated jaxlib to version 0.1.76.
• Updated numpy to version 1.21.4.
• Removed retrying.
• Updated tensorflow-model-optimization to version 0.7.1.
• Updated tensorflow-model-optimization to version 0.7.3.
• Updated tensorflow to version 2.8.0.
• Added support for building many dependencies including tensorflow using
  Bazel.
• Updated the Bazel dependencies:
• Updated rules_python to version 0.5.0.
• Updated com_google_protobuf to version v3.18.0-rc1.
• Added absl_py version 1.0.0.
• Added com_google_googletest version release-1.11.0.
• Added io_bazel_rules_go version v0.29.0.
• Added bazel_skylib version 1.0.3.
• Added pybind11_abseil.
• Added pybind11_bazel.
• Added pybind11_protobuf.
• Added com_google_absl version 20211102.0.
• Added tensorflow_org version v2.8.0.

Breaking Changes

• Removed support for building source on MacOS.
• Removed support for Python 3.6.
• Removed symbol tff.framework.type_contains, use tff.types.contains
  instead.
• Removed many symbols from tff.simulation, these can be found in
  tff.program instead.
• Removed support for converting non-OrderedDict mapping types to
  tff.Values.
• Removed tff.simulation.datasets.ClientData.from_clients_and_fn in favor of
  tff.simulation.datasets.ClientData.from_clients_and_tf_fn.
• Restricted tff.simulation.datasets.ClientData.preprocess to only support
  TF-serializable functions.
• Removed tff.backends.reference, and the reference context it contained.
• Removed tff.learning.build_federated_sgd_process in favor of
  tff.learning.algorithms.build_fed_sgd.
• Removed tff.simulation.run_simulation in favor of
  tff.simulation.run_training_process.
• Removed tff.learning.framework.EnhancedModel.
• Removed tff.learning.framework.build_stateless_mean.

Bug Fixes

• Fixed broken links in documentation.
• Fixed many pytype errors.
• Fixed some inconsistencies in Bazel visibility.
• Fixed bug where tff.simulation.datasets.gldv2.load_data() would result in
  an error.

Release 0.19.0

Major Features and Improvements

• Introduced new intrinsics: federated_select and federated_secure_select.
• New tff.structure_from_tensor_type_tree to help manipulate structures of
  tff.TensorType into structures of values.
• Many new tff.aggregators factory implementations.
• Introduced tf.data concept for data URIs.
• New tff.type package with utilities for working with tff.Type values.
• Initial experimental support for tff.jax_computation.
• Extend tff.tf_computation support to SpareTensor and RaggedTensor.

Breaking Changes

• Update gRPC dependency to 1.34.
• Moved ClientData interface and implementations to
  tff.simulation.datasets.
• Renamed tff.utils.update_state to tff.structure.update_struct.
• Removed the tff.utils namespace, all symbols have migrated, many to
  tff.aggregators.
• Moved infinite EMNIST dataset to federated research repository.
• Removes rpc_mode argument to remote executors, along with streaming mode.
• Removes deprecated tff.federated_apply.
• Removes tff.federated_reduce, all usages can use
  tff.federated_aggregate.
• Removes HDF5ClientData and h5py pip dependency.
• Removes setattr functionality on tff.ValueImpl.

Bug Fixes

• Improved tf.GraphDef comparisons.
• Force close generators used for sending functions to computation wrappers,
  avoiding race conditions in Colab.
• Fix tracing libraries asyncio usage to be Python3.9 compatible.
• Fix issue with destruction of type intern pool destructing and abc.
• Fix type interning for tensors with unknown dimensions.
• Fix ClientData.create_dataset_from_all_clients consuming unreasonable
  amounts of memory/compute time.

Release 0.18.0

Major Features and Improvements

• Extended the tff.simulation package to add many new tools for running
  simulations (checkpoints and metrics managers, client sampling functions).
• Extended the tff.aggregators package with a number of new aggregation
  factories.
• Added the tff.structure API to expose the Struct class and related
  functions.
• Added the tff.profiler API to expose useful profiling related functions.
• Added the tff.backends.test package to expose backends that focused on
  testing specifically a way to test a computation with a
  federated_secure_sum intrinsic.
• Added the tff.experimental package to expose less stable API.

Breaking Changes

• Replaced the tff.aggregators.AggregationProcessFactory abstract base class
  with the tff.aggregators.UnweightedAggregationFactory and the
  tff.aggregators.WeightedAggregationFactory classes.
• Replaced the tff.aggregators.ZeroingFactory class with a
  tff.aggregators.zeroing_factory function with the same input arguments.
• Replaced the tff.aggregators.ClippingFactory class with a
  tff.aggregators.clipping_factory function with the same input arguments.
• Updated tensorflow package dependency to 2.4.0.
• Updated absl-py package dependency to 0.10.
• Updated grpcio package dependency to 1.32.0.
• Added a jaxlib package dependency at 0.1.55.
• Updated numpy package dependency to 1.19.2.
• Updated tensorflow-addons package dependency to 0.12.0.
• Updated tensorflow-model-optimization package dependency to 0.5.0.

Bug Fixes

• Fixed issue with the sequence_reduce intrinsic handling federated types.

Major Features and Improvements

• New tff.aggregators package with interfaces for stateful aggregation
  compositions.
• New Google Landmark Dataset tff.simulations.dataset.gldv2
• New convenience APIs tff.type_clients and tff.type_at_server
• Invert control of computation tracing methods to produce clearer Python
  stack traces on error.
• Move executor creation to a factory pattern in executor service, allowing
  distributed runtimes to be agnostic to number of clients.
• Significant improvements of type serialization/deserialization
• New tff.simulations.compose_dataset_computation_with_iterative_process API
  to move execution of client dataset construction to executor stack leaves.
• Extend parameterization of tff.learning.build_federated_averaging_process
  with use_experimental_simulation_loop argument to better utilize multi-GPU
  setups.

Breaking Changes

• Removed tff.utils.StatefulFn, replaced by tff.templates.MeasuredProcess.
• Removed tff.learning.assign_weights_to_keras_model
• Stop removing OptimizeDataset ops from tff.tf_computations.
• The research/ directory has been moved to
  http://github.com/google-research/federated.
• Updates to input_spec argument for tff.learning.from_keras_model.
• Updated TensorFlow dependency to 2.3.0.
• Updated TensorFlow Model Optimization dependency to 0.4.0.

Bug Fixes

• Fixed streaming mode hang in remote executor.
• Wrap collections.namedtuple._asdict calls in collections.OrderedDict to
  support Python 3.8.
• Correctly serialize/deserialize tff.TensorType with unknown shapes.
• Cleanup TF lookup HashTable resources in TFF execution.
• Fix bug in Shakespeare dataset where OOV and last vocab character were the
  same.
• Fix TFF ingestion of Keras models with shared embeddings.
• Closed hole in compilation to CanonicalForm.

Known Bugs

• "Federated Learning for Image Classification" tutorial fails to load
  projector plugin for tensorboard (https://github.com/tensorflow/federated/issues/914).
• Certain Keras models with activity regularization fail in execution with
  unliftable error (https://github.com/tensorflow/federated/issues/913).

Thanks to our Contributors

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

@amitport, @ronaldseoh

Release 0.16.1

Bug Fixes

• Fixed issue preventing Python lists being all_equal values.

Release 0.16.0

Major Features and Improvements

• Mirrored user-provided types and minimize usage of AnonymousTuple.

Breaking Changes

• Renamed AnonymousTuple to Struct.

Release 0.15.0

Major Features and Improvements

• Updated tensorflow-addons package dependency to 0.9.0.
• Added API to expose the native backend more conveniently. See
  tff.backends.native.* for more information.
• Added a compiler argument to the tff.framework.ExecutionContext API and
  provided a compiler for the native execution environment, which improves
  TFF’s default concurrency pattern.
• Introduced a new tff.templates.MeasuredProcess concept, a specialization
  of tff.templates.IterativeProcess.
• Extends tff.learning interfaces to accept tff.templates.MeasuredProcess
  objects for aggregation and broadcast computations.
• Introduce new convenience method tff.learning.weights_type_from_model.
• Introduced the concept of a tff.framework.FederatingStrategy, which
  parameterizes the tff.framework.FederatingExecutor so that the
  implementation of a specific intrinsic is easier to provide.
• Reduced duplication in TFF’s generated ASTs.
• Enabled usage of GPUs on remote workers.
• Documentation improvements.

Breaking Changes

• The IterativeProcess return from
  tff.learning.build_federated_averaging_process and
  tff.learning.build_federated_sgd_process now zip the second tuple output
  (the metrics) to change the result from a structure of federated values to
  to a federated structure of values.
• Removed tff.framework.set_default_executor function, instead you should
  use the more convenient tff.backends.native.set_local_execution_context
  function or manually construct a context an set it using
  tff.framework.set_default_context.
• The tff.Computation base class now contains an abstract __hash__ method,
  to ensure compilation results can be cached. Any custom implementations of
  this interface should be updated accordingly.

Bug Fixes

• Fixed issue for missing variable initialization for variables explicitly not
  added to any collections.
• Fixed issue where table initializers were not run if the
  tff.tf_computation decorated function used no variables.

Thanks to our Contributors

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

jvmcns@

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.

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

• 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