Metadata-Version: 2.1
Name: tensorflow-federated
Version: 0.2.0
Summary: TensorFlow Federated is an open-source federated learning framework.
Home-page: http://tensorflow.org/federated
Author: Google Inc.
Author-email: packages@tensorflow.org
License: Apache 2.0
Download-URL: https://github.com/tensorflow/federated/tags
Keywords: tensorflow federated machine learning
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/plain
Requires-Dist: h5py (~=2.6.0)
Requires-Dist: numpy (~=1.14)
Requires-Dist: six (~=1.10)
Requires-Dist: tensorflow (~=1.13)

TensorFlow Federated (TFF) is an open-source framework for machine learning and
other computations on decentralized data. TFF has been developed to facilitate
open research and experimentation with Federated Learning (FL), an approach to
machine learning where a shared global model is trained across many
participating clients that keep their training data locally. For example, FL has
been used to train prediction models for mobile keyboards without uploading
sensitive typing data to servers.

TFF enables developers to use the included federated learning algorithms with
their models and data, as well as to experiment with novel algorithms. The
building blocks provided by TFF can also be used to implement non-learning
computations, such as aggregated analytics over decentralized data.

TFF's interfaces are organized in two layers:

* Federated Learning (FL) API

  The `tff.learning` layer offers a set of high-level interfaces that allow
  developers to apply the included implementations of federated training and
  evaluation to their existing TensorFlow models.

* Federated Core (FC) API

  At the core of the system is a set of lower-level interfaces for concisely
  expressing novel federated algorithms by combining TensorFlow with distributed
  communication operators within a strongly-typed functional programming
  environment. This layer also serves as the foundation upon which we've built
  `tff.learning`.

TFF enables developers to declaratively express federated computations, so they
could be deployed to diverse runtime environments. Included with TFF is a
single-machine simulation runtime for experiments. Please visit the
tutorials and try it out yourself!


