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.