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.