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# XAIN

The XAIN project is building a privacy layer for machine learning so that AI projects can meet compliance such as
GDPR and CCPA. The approach relies on Federated Learning as enabling technology that allows production AI
applications to be fully privacy compliant.

Federated Learning also enables different use-cases that are not strictly privacy related such as connecting data
lakes, reaching higher model performance in unbalanced datasets and utilising AI models on the edge.

This repository contains the source code for running the Coordinator. The Coordinator is the component of Federated
Learning that selects the Participants for training and aggregates the models using federated averaging.

The Participants run in a separate environment than the Coordinator and connect to it using an SDK. You can find [here](https://github.com/xainag/xain-sdk) the source code for it.

## Quick Start

XAIN requires [Python 3.6.4+](https://python.org/). To install the `xain-fl` package just run:

```shell
$ python -m pip install xain-fl
```

## Install from source

Clone this repository:

```shell
git clone https://github.com/xainag/xain-fl.git
```

Install this project with the `dev` profile (**NOTE**: it is
recommended to install the project in a virtual environment):

```shell
cd xain-fl
pip install -e '.[dev]'
```

Verify the installation by running the tests

```shell
pytest
```

### Building the Documentation

The project documentation resides under `docs/`. To build the documentation
run:

```shell
$ cd docs/
$ make docs
```

The generated documentation will be under `docs/_build/html/`. You can open the
root of the documentation by opening `docs/_build/html/index.html` on your
favorite browser or simply run the command:

```shell
$ make show
```

### Running the Coordinator locally

To run the Coordinator on your local machine, you can use the
`example-config.toml` file:

```shell
# If you have installed the xain_fl package,
# the `coordinator` command should be directly available
coordinator --config configs/example-config.toml

# otherwise the coordinator can be started by executing the
# `xain_fl` package:
python xain_fl --config configs/example-config.toml
```

### Run the Coordinator from a Docker image

There are two docker-compose files, one for development and one for release.

#### Development image

To run the coordinator's development image, first build the Docker image:

```shell
$ docker build -t xain-fl-dev -f Dockerfile.dev .
```

Then run the image, mounting the directory as a Docker volume:

```shell
$ docker run -v $(pwd):/app -v '/app/xain_fl.egg-info' xain-fl-dev coordinator
```

The command above uses a default configuration but you can also use a
custom config file:

For instance, if you have a `./custom_config.toml` file that you'd
like to use, you can mount it in the container and run the coordinator
with:

```shell
docker run \
  -v $(pwd)/custom_config.toml:/custom_config.toml \
  -v $(pwd):/app \
  -v '/app/xain_fl.egg-info' \
  xain-fl-dev \
  coordinator --config /custom_config.toml
```

#### Release image

To run the coordinator's release image, first build it:

```shell
$ docker build -t xain-fl .
```

And then run it (this example assumes you'll want to use the default port):

```shell
$ docker run -p 50051:50051 xain-fl
```

### Docker-compose

The coordinator needs a storage service that provides an AWS S3
API. For development, we use `minio`. We provide `docker-compose`
files that start coordinator container along with a `minio` container,
and pre-populate the appropriate storage buckets.

#### Development

To start both the coordinator and the `minio` service use:

```shell
docker-compose -f docker-compose-dev.yml up
```

It is also possible to only start the storage service:

```shell
docker-compose -f docker-compose-dev.yml up minio-dev initial-buckets
```

#### Release

```shell
$ docker-compose up
```

## Related Papers and Articles

- [An introduction to XAIN’s GDPR-compliance Layer for Machine Learning](https://medium.com/xain/an-introduction-to-xains-gdpr-compliance-layer-for-machine-learning-f7c321b31b06)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://arxiv.org/abs/1602.05629)
- [Analyzing Federated Learning through an Adversarial Lens](https://arxiv.org/abs/1811.12470)
- [Towards Federated Learning at Scale: System Design](https://arxiv.org/abs/1902.01046)