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https://github.com/google-parfait/tensorflow-federated

An open-source framework for machine learning and other computations on decentralized data.
https://github.com/google-parfait/tensorflow-federated

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An open-source framework for machine learning and other computations on decentralized data.

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# 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)](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html),

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](https://arxiv.org/abs/1811.03604)

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](https://github.com/google-parfait/tensorflow-federated/blob/main/docs/federated_learning.md)

    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](https://github.com/google-parfait/tensorflow-federated/blob/main/docs/federated_core.md)

    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!



## Installation



See the

[install](https://github.com/google-parfait/tensorflow-federated/blob/main/docs/install.md)

documentation for instructions on how to install TensorFlow Federated as a

package or build TensorFlow Federated from source.



## Getting Started



See the

[get started](https://github.com/google-parfait/tensorflow-federated/blob/main/docs/get_started.md)

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 in `tff.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 the `research` directory 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](http://www.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.learning` API, 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](https://github.com/google-parfait/tensorflow-federated/blob/main/CONTRIBUTING.md#guidelines)

guidelines on how to contribute.



## Issues



Use

[GitHub issues](https://github.com/google-parfait/tensorflow-federated/issues)

for tracking requests and bugs.



## Questions



Please direct questions to [Stack Overflow](https://stackoverflow.com) using the

[tensorflow-federated](https://stackoverflow.com/questions/tagged/tensorflow-federated)

tag.