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Created with Raphaël 2.2.03Mar2128Feb2726252423222119181718171613121110965432129Jan2827262522211915141312118765430Dec292824232221171615141198753130Nov2726252423201918171612111096543230Oct292827232221201917161514131211987652129Sep2825242322211916151413121110986432128Aug27262521201918171514131210765Adds possibility of unweighted aggregation to tff.learning.secure_aggregator.Adds possibility of unweighted aggregation to tff.learning.compression_aggregator.Skips the first notebook cell when testing.Adds possibility of unweighted aggregation to tff.learning.robust_aggregator.Expose `UnweightedReservoirSamplingFactory` in the `tff.aggregators` package.Expose `SqlClientData` in the pip package public API.Remove selection-by-name support from executorsUse zip for grads and vars in apply_gradients in simple_fedavg.Adds more detailed description of the effects of the `clipping` and `zeroing` arguments to aggregators in model_update_aggregator.py.Add an UnweightedAggregationFactory that performs unweighted reservoir sampling using `tff.federated_aggregate`.Fix a typo in `update_state()` description.Update tff docs to describe tff.aggregators for differentially private aggregation.Finish cleaning up the `impl` package.Move the `tree_to_cc_transformations_test` module to the `impl` package.Extend `tff.utils.update_state` for work on `tff.structure.Struct` inputs.Updates TFF callsites for graphdef equality.Flatten selections to index in computation.protoTransformingClientData does not add suffixes if not creating pseudo-clients.Adds a small tutorial on JAX support.Remove unused GCP endpoint scripts.Automated rollback of commit bf7f333585591101ce578b782d3cf7dbf45db2a5TransformingClientData defaults to having same number of clients as original data.Adds AggregationProcess.is_weighted property.Uses new AggregationProcess.is_weighted property.cl/359576676 up…cl/359576676 upstream/cl/359576676Factors out federated averaging for JAX as a reusable component to significantly shorten the JAX/XLA training example.Adds download and other links as appropriate to TFF tutorials.Updates the JAX/XLA example to use tff.federated_mean.Adds JAX/XLA support for tff.federated_sum and friends.Completes the implementation of XLA local computation factory.First step towards decoupling federated executors from TF, injects local computation factory into the federated strategies (still defaulting to TF, but configurable by the user). Also, completes the local computation factory ABC, and updates the return type.Open-sources working with ClientData tutorial.Consistently use PyPI distribution names.Allow non-lambdas to transform_to_local_call_dominantAdds check for TensorFlow computation invocation in TF Computation context.Constructor for constant XLA computations (for use with tff.federated_aggregate).Factors out computation callable from XLA executor (for independent use in tests).Consolidates XLA code in backends/xla.Moves the XLA backend out of experimental.Add transform_to_local_call_dominantRemoves gating for resolve_higher_order_functions, since quadratic complexity is understood and prevented.