Tensorflow-federated

Major Features and Improvements

* Extend `tff.analytics.heavy_hitters.iblt` with `create_chunker` API for
encoding non-Unicode strings.
* Extend `tff.aggregators.DifferentiallyPrivateFactory.tree_aggregation` with
an optional `record_aggregation_factory` argument.

Breaking Changes

* Replaced `ModularClippingSumFactory` with `SecureModularSumFactory` in
`tff.analytics.build_hierarchical_histogram_process`.

Known Bugs

* TFF's python 3.9 typing conflicts with Colab's Python 3.7 support.

Major Features and Improvements

* Add a MimeLite implementation that allows from optimizer learning rate
scheduling in
`tff.learning.algorithms.build_mime_lite_with_optimizer_schedule`.

Breaking Changes

* None

Bug Fixes

* None

Known Bugs

* TFF's python 3.9 typing conflicts with Colab's Python 3.7 support.

Major Features and Improvements

* Added `ReleaseManager`s to make authoring program logic more convenient.
* Updated TFFs `attrs` dependency to version `21.4.0`.
* Update TFFs `tensorflow-privacy` dependency to version `0.8.1`.

Breaking Changes

* Changed `tff.learning.BatchOutput` from an attrs class to a namedtuple.
* Removed unused `tff.learning.framework.parameter_count_from_model` API.

Major Features and Improvements

* Add tests for `namedtuple`s in the `tff.program` package.
* Add `num_subrounds` parameter to the mergeable context, allowing callers to
optionally sequentialize subrounds.
* Add metrics support to `tff.learning.models.FunctionalModel`, including
updates to the helper function `create_functional_metric_fns` and the
downstream caller `tff.learning.algorithms.build_weighted_fed_avg`.

Bug Fixes

* Fix typo in the types constructed for testing the `tff.program` package.
* Fix some program example typos.
* Fix tests that don't seem to be running under the CI.
* Fix naming bug for Python mergeable execution.
* Ensure exceptions raised from remote executor stub implement gRPC error
interface.
* Update `tff.structure.Struct` integration with JAX pytrees to not flatten
the entire structure.
* Use Python 3.7 compatible type annotations until Colab updates to Python
3.9.

Major Features and Improvements

* Update the `MemoryReleaseManager` to save `type_signature` when releasing
values.
* Add a `type_signature` parameter to the `ReleaseManager.release` method.
* Unify retryability logic between TFF-C++ and TFF-Python.
* Update the TFF contributions and collaboration links to point to the Discord
server.

Breaking Changes

* Move Python executor stacks file to `python_executor_stacks.py` in
`executor_stacks` directory.

Bug Fixes

* Ensure that dataset deserialization will return ragged and sparse tensors,
as needed according to the TFF type of the dataset.
* Make `metric_finalizers` use metric constructors if available.

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.