Metadata-Version: 2.1
Name: tensorflow-federated
Version: 0.1.0
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
Requires-Dist: numpy
Requires-Dist: six
Requires-Dist: tensorflow (>=1.13.0rc2)

TensorFlow Federated (TFF) is an open-source framework for collaborative
computations on distributed data that does not require collecting data at a
centralized location.

The framework has initially been developed to facilitate open research and
experimentation with
[Federated Learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html),
a technology that enables devices owned by end users to collaboratively learn a
shared prediction model while keeping potentially sensitive training data on the
devices, thus decoupling the ability to do machine learning from the need to
collect and store the data in the cloud.

With the interfaces provided by TFF, developers can test existing federated
learning algorithms on their models and data, or design new experimental
algorithms and run them on existing models and data, all within the same open
source environment. The framework has been designed with compositionality in
mind, and can be used to combine independently-developed techniques and
components that offer complementary capabilities into larger systems.


