kairouzp / federated

A collection of Google research projects related to Federated Learning and Federated Analytics.

Federated Research

Federated Research is a collection of research projects related to Federated Learning and Federated Analytics. Federated learning is an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally. Federated analytics is the practice of applying data science methods to the analysis of raw data that is stored locally on users’ devices.

Many of the projects contained in this repository use TensorFlow Federated (TFF), an open-source framework for machine learning and other computations on decentralized data. For an overview and introduction to TFF, please see the list of tutorials. For information on using TFF for research, see TFF for research.

Contributing

This repository contains Google-affiliated research projects related to federated learning and analytics. If you are working with Google collaborators and would like to feature your research project here, please review the contribution guidelines for guidelines on coding style, best practices, etc.

Pull Requests

We currently do not accept pull requests for this repository. If you have feature requests or encounter a bug, please file an issue to the project owners.

Issues

Please use GitHub issues to communicate with project owners for requests and bugs. Add [project/folder name] in the issue title so that we can easily find the best person to respond.

Questions

If you have questions related to TensorFlow Federated, please direct your questions to Stack Overflow using the tensorflow-federated tag.

If you would like more information on federated learning, please see the following introduction to federated learning. For a more in-depth discussion of recent progress in federated learning and open problems, see Advances and Open Problems in Federated Learning.