TensorFlow Federated
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.learninglayer 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 builttff.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!
Installation
See the install documentation for instructions on how to
install TensorFlow Federated as a package or build TensorFlow Federated from
source.
Getting Started
See the get started documentation for instructions on
how to use TensorFlow Federated.
Contributing
There are a number of ways to contribute depending on what you're interested in:
-
If you are interested in developing new federated learning algorithms, the
best way to start would be to study the implementations of federated
averaging and evaluation intff.learning, and to think of extensions to
the existing implementation (or alternative approaches). If you have a
proposal for a new algorithm, we recommend starting by staging your project
in theresearchdirectory and including a colab notebook to showcase the
new features.You may want to also develop new algorithms in your own repository. We are
happy to feature pointers to academic publications and/or repos using TFF on
tensorflow.org/federated. -
If you are interested in applying federated learning, consider contributing
a tutorial, a new federated dataset, or an example model that others could
use for experiments and testing, or writing helper classes that others can
use in setting up simulations. -
If you are interested in helping us improve the developer experience, the
best way to start would be to study the implementations behind the
tff.learningAPI, and to reflect on how we could make the code more
streamlined. You could contribute helper classes that build upon the FC API
or suggest extensions to the FC API itself. -
If you are interested in helping us develop runtime infrastructure for
simulations and beyond, please wait for a future release in which we will
introduce interfaces and guidelines for contributing to a simulation
infrastructure.
Please be sure to review the
contribution guidelines
for guidelines on the coding style, best practices, etc.
Compatibility
The following table describes the compatibility between the TensorFlow Federated
and TensorFlow Python packages.
TensorFlow Federated, TensorFlow
--------------------------------------------------------------, ----------
0.12.0, tensorflow 2.0.0
0.11.0, tensorflow 2.0.0
0.10.1, tensorflow 2.0.0
0.10.0, tensorflow 2.0.0
0.9.0, tf-nightly 2.1.0.dev20191005
0.8.0, tf-nightly 1.15.0.dev20190805
0.7.0, tf-nightly 1.15.0.dev20190711
0.6.0, tf-nightly 1.15.0.dev20190626
0.5.0, tf-nightly 1.14.1.dev20190528
0.4.0, tensorflow 1.13.1
0.3.0, tensorflow 1.13.1
0.2.0, tensorflow 1.13.1
0.1.0, tensorflow 1.13.0rc2
Issues
Use GitHub issues for tracking
requests and bugs.
Questions
Please direct questions to Stack Overflow using the
tensorflow-federated
tag.