FATE

FATE (Federated AI Technology Enabler) is an open-source project initiated by Webank's AI Department to provide a secure computing framework to support the federated AI ecosystem. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). It supports federated learning architectures and secure computation of various machine learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning.

https://www.fedai.org/

Install

FATE can be installed on Linux or Mac. Now, FATE can support standalone and cluster deployments.

Standalone

Docker version

FATE $ sh build_standalone_docker.sh
FATE $ CONTAINER_ID=`docker run -t -d  fate/standalone`
FATE $ docker exec -t -i ${CONTAINER_ID} bash

There are a few algorithms under examples/ folder, try them out!

Manual version

FATE (venv) $ pip install -r requirements.txt
FATE (venv) $ export PYTHONPATH=`pwd -P`

Cluster

FATE also provides a distributed runtime architecture for Big Data scenario. Migration from standalone to cluster requires configuration change only. No algorithm change is needed.

To deploy FATE on a cluster, please refer to cluster deploy guide at cluster-deploy.

Running Tests

A script to run all the unittests has been provided in ./federatedml/test folder.

Once FATE is installed, tests can be run using:

sh ./federatedml/test/run_test.sh

All the unittests shall pass if FATE is installed properly.

Example Programs

Start Programs

Fate also provides several sample programs in the examples directory for each algorithm. You can check out for the detailed documentation in each specific algorithm directory. Here, we take homogeneous LR as an example. In conf directory, we have provided several json file as configuration templates. The load_file.json is used for loading data into distributed database. The three runtime_conf.json are configuration files for arbiter, guest and hosts respectively. Please check out the meaning of each parameter in out documentation.

For quick start our program, we provide a standalone version which simulate three party in one machine. To start standalone version, please run the prepared shell file like:

sh run_logistic_regression_standalone.sh

Boom, a HomoLR program has been started up. This program will use the setting in configuration files. Please note that the parameters in algorithm part (LogsiticParam for HomoLR, for example) are supposed to be identity among three parties.

As for a real clustering version, we have also provided a quick start script which named run_XXX_cluster.sh (run_logistic_regression_cluster.sh for HomoLR) But this time, you need to specify the role, jobid and partyid for three parties. Then run the following three instructions in the corresponding party respectively:

sh run_logistic_regression_cluster.sh guest $jobid $guestpartyid $hostpartyid $arbiterpartyid

sh run_logistic_regression_cluster.sh host $jobid $guestpartyid $hostpartyid $arbiterpartyid

sh run_logistic_regression_cluster.sh arbiter $jobid $guestpartyid $hostpartyid $arbiterpartyid

Check log files

The logs are provided in the logs directory. A sub-directory will be generated with the name of jobid. All the log files are listed in this directory. The trained and validation result are shown in workflow.log. Feel free to check out each log file for more training details.

Getting Involved

• Join us on google groups Fate-FedAI Group. You can ask questions and participate in the development discussion.

• For any frequently asked questions, you can check in FAQ.

• Please report bugs by submitting issues.

• Submit contributions using pull requests

License

Apache License 2.0