TensorFlow Federated 0.28.0

Release 0.28.0

Major Features and Improvements

• Updated tutorials to use tff.learning.algorithms API.
• Asynchronous TFF execution contexts no longer assume a single global
  cardinality; they concurrently invoke any computation for which concurrency
  is requested.

Breaking Changes

• Removed tff.learning.build_federated_averaging_process; users should
  migrate to tff.learning.algorithms.build_weighted_fed_avg.

Bug Fixes

• Clarified semantics for TFF-C++ multimachine Dispose, DisposeExecutor,
  and executor keying, to avoid raising exceptions and spamming logs in the
  course of normal operation.
• Fixed unsigned integer overflow for TFF-C++
  max_concurrent_computation_calls.
• Normalizes on call-dominant form before attempting to compile to
  MergeableCompForm, removing spurious failures on dependent-aggregation
  checking.

Known Bugs

• Serialization / deserialization of tf.data.Datasets yielding non-dense
  tensors for multimachine runtime may encounter issues:
  • tff.framework.deserialize_value may fail to deserialize
    tf.data.Datasets yielding RaggedTensors or SparseTensors.
  • tff.framework.serialize_value may fail to serialize tf.data.Datasets
    yielding SparseTensors.

TensorFlow Federated 0.27.0

Release 0.27.0

Major Features and Improvements

• New Colab notebook illustrating how to use DataBackend to load remote datasets.
• Added a CreateDataDescriptor helper function.
• Added a worker binary serving the TFF-C++ executor service.

Bug Fixes

• Fixed bug with intermediate aggregation and controller failures, causing hangs.

TensorFlow Federated 0.26.0

Release 0.26.0

Major Features and Improvements

• Updated TensorFlow to 2.9.1.
• Update pybind11 to 2.9.2.
• Re-enable cpp_fast_protos.
• Introduces container class to run coroutines in a dedicated thread, allowing
  TFF’s synchronous execution interfaces to be used in conjunction with other
  asyncio code.
• Use latest TFF version in Colab notebook links.
• Rename the helper functions that create test MeasuredProcesses.
• Add a compiler transform checking Tensorflow computations against list of
  allowed ops.
• Explicitly specify return types in the program package.
• Adds convenience function for setting a local async CPP execution context.
• Move jax components into a non-experimental namespace.

Breaking Changes

• Switch compilation flag _GLIBCXX_USE_CXX11_ABI to 1.

TensorFlow Federated 0.25.0

Release 0.25.0

Major Features and Improvements

• Adds error message logging to TFF C++ execution context.
• Adds test coverage for C++ runtime with aggregators.
• Redefines 'workers going down with fixed clients per round' test.
• Add complete examples of using DataBackend with TFF comps.
• Updated the MapReduceForm documentation to include the two additional secure
  sum intrinsics.
• tff.learning
  • Relax the type check on LearningProcess from strictly SequenceType to
    also allow structures of SequenceType.

Breaking Changes

• Remove usage of tff.test.TestCase, tff.test.main(), and delete
  test_case module.
• Update test utility docstrings to use consistent vocabulary.
• Update to TensorFlow 2.9.0
• Rename up compiler/test_utils to compiler/building_block_test_utils.
• Remove some unnecessary usage of pytype: skip-file.
• Specify the None return type of ReleaseManager.release.
• Remove usage of deprecated numpy types.
• Replace depreciated random_integers with randint.

Bug Fixes

• Fix numpy warning.

TensorFlow Federated 0.24.0

Release 0.24.0

Major Features and Improvements

• Added asyncio.run call to metrics manager release calls to ensure
  compatibility with a98b5ed.
• Added an example and documentation for the Federated Program API.
• Improved model_update_aggregator to support structures with mixed floating dtypes.
• Create a mirror of tff.simulation.compose_dataset_computation_with_iterative_process for tff.learning.templates.LearningProcess.
• Added logging of expected sequential computations to local TFF-C++ runtime.

Breaking Changes

• Moved asserts from tff.test.TestCase to tff.test.* as functions.
• Removed assert_type_assignable_from function.
• Moved assert_nested_struct_eq to the type_conversions_test module.
• Removed client_train_process and fedavg_ds_loop comparisons.

Bug Fixes

• Fixed comparisons to enums in the benchmarks package.
• Fixed async_utils.SharedAwaitable exception raiser.
• Fixed various lint errors.

TensorFlow Federated 0.23.0

Release 0.23.0

Major Features and Improvements

• Deprecated tff.learning.build_federated_averaging_process.
• Added an API to convert tf.keras.metrics.Metric to a set of pure
  tf.functions.

Breaking Changes

• Renamed ProgramStateManager.version to ProgramStateManager.get_versions.

Bug Fixes

• Fixed the "datasets/" path in the working with TFF's ClientData tutorial.

TensorFlow Federated 0.22.0

Release 0.22.0

Major Features and Improvements

• Updated .bazelversion to 5.1.1.
• Updated the tff.program API to use asyncio.
• Exposed new APIs in the tff.framework package:
  • tff.framework.CardinalitiesType.
  • tff.framework.PlacementLiteral.
  • tff.framework.merge_cardinalities.
• tff.analytics
  • Added new analytic_gauss_stddev API.

Breaking Changes

• Renamed ProgramStateManager.version to ProgramStateManager.get_versions.

Bug Fixes

• Fixed some Python lint errors related to linting Python 3.9.
• Cleaned up stale TODOs throughout the codebase.

Known Bugs

• Version 0.21.0 currently fails to import in
  colab if the version of Python is less
  than Python 3.9. Please use a runtime with a version of Python greater than
  Python 3.9 or use TFF version 0.20.0.

TensorFlow Federated 0.21.0

Release 0.21.0

Major Features and Improvements

• tff.analytics
  • Added new tff.analytics.IbltFactory aggregation factory.
  • Added new IBTL tensor encoder/decoder libraries and uses them in
    tff.analytics.heavy_hitters.iblt.build_iblt_computation.
• tff.aggregator
  • Added as_weighted_aggregator to the tff.aggregator.Factory API.
• tff.learning
  • Improved compilation and execution performance of
    tff.learning.metrics.secure_sum_then_finalize by grouping tensors by
    DType.
  • Added set_model_weights method and default implementation to
    tff.learning.templates.LearningProcess.
  • Added a new reset_metrics attribute to tff.learning.Model.
  • Added schedule_learning_rate to tff.learning.optimizers.
  • Added new tff.learning.ddp_secure_aggregator for Distributed
    Differential Privacy.
• tff.simulation
  • Added an option to distort train images in the CIFAR-100 baseline task.
  • Changed the default sequence length for the Shakespeare baseline task to
    a more reasonable value.
• Core
  • Switched runtime to create new RemoteExecutors with different
    cardinalities, rather than resetting the cardinality in the remote
    service.

Breaking Changes

• Removed support for Python 3.7 and 3.8, TFF supports 3.9 and later.
• Removed deprecated attributes report_local_outputs and
  federated_output_computation from tff.learning.Model
• Removed the ingest method from tff.Context

Bug Fixes

• Multiple typos in tests, code comments, and pydoc.

Known Bugs

• Sequences (datasets) of SparseTensors don't work on the C++ runtime.
• Computations when CLIENTS cardinality is zero doesn't work on the Python
  runtime.
• Assigning variables to a Keras model after construction inside a model_fn
  results in a non-deterministic graph.

TensorFlow Federated v0.20.0

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.

TensorFlow Federated 0.19.0

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.