Flower Federations with Authentication 🧪

[!NOTE] 🧪 = This example covers experimental features that might change in future versions of Flower. Please consult the regular PyTorch examples (quickstart, advanced) to learn how to use Flower with PyTorch.

The following steps describe how to start a long-running Flower server (SuperLink) and a long-running Flower clients (SuperNode) with authentication enabled. The task is to train a simple CNN for image classification using PyTorch.

[!TIP] Follow this how-to guide to learn more about Flower’s Deployment Engine, how setting up secure TLS-enabled communications and SuperNode authentication works. If you are already familiar with how the Deployment Engine works, you may want to learn how to run this same example using Docker. Check out the Flower with Docker documentation.

Project Setup

Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you:

git clone --depth=1 https://github.com/adap/flower.git _tmp \
        && mv _tmp/examples/flower-authentication . \
        && rm -rf _tmp && cd flower-authentication

This will create a new directory called flower-authentication with the following project structure:

flower-authentication
├── authexample
│   ├── __init__.py
│   ├── client_app.py   # Defines your ClientApp
│   ├── server_app.py   # Defines your ServerApp
│   └── task.py         # Defines your model, training and data loading
├── pyproject.toml      # Project metadata like dependencies and configs
├── certificate.conf    # Configuration for OpenSSL
├── generate.sh         # Generate certificates and keys
├── prepare_dataset.py  # Generate datasets for each SuperNode to use
└── README.md

Install dependencies and project

Install the dependencies defined in pyproject.toml as well as the authexample package.

pip install -e .

Generate TLS certificates

The generate_cert.sh script generates certificates for creating a secure TLS connection between the SuperLink and SuperNodes, as well as between the flwr CLI (user) and the SuperLink.

[!NOTE] Note that this script should only be used for development purposes and not for creating production key pairs.

./generate_cert.sh

Generate public and private keys for SuperNode authentication

The generate_auth_keys.sh script generates two private–public key pairs for two SuperNodes by default. If you have more SuperNodes, you can specify the number of key pairs to generate.

[!NOTE] Note that this script should only be used for development purposes and not for creating production key pairs. The script also generates a CSV file that includes each of the generated (client) public keys.

# Generate two key pairs by default
./generate_auth_keys.sh

# Or pass the desired the number of key pairs
./generate_auth_keys.sh {your_number_of_clients}

Start the long-running Flower server (SuperLink)

Starting long-running Flower server component (SuperLink) and enable authentication is very easy; all you need to do is type --auth-list-public-keys containing file path to the known client_public_keys.csv. Notice that you can only enable authentication with a secure TLS connection.

Let’s first launch the SuperLink:

flower-superlink \
    --ssl-ca-certfile certificates/ca.crt \
    --ssl-certfile certificates/server.pem \
    --ssl-keyfile certificates/server.key \
    --auth-list-public-keys keys/client_public_keys.csv

At this point your server-side is idling. Next, let’s connect two SuperNodes, and then we’ll start a run.

Start the long-running Flower client (SuperNode)

[!NOTE] Typically each SuperNode runs in a different entity/organization which has access to a dataset. In this example we are going to artificially create N dataset splits and saved them into a new directory called datasets/. Then, each SuperNode will be pointed to the dataset it should load via the --node-config argument. We provide a script that does the download, partition and saving of CIFAR-10.

python prepare_dataset.py

In a new terminal window, start the first long-running Flower client (SuperNode):

flower-supernode \
    --root-certificates certificates/ca.crt \
    --auth-supernode-private-key keys/client_credentials_1 \
    --auth-supernode-public-key keys/client_credentials_1.pub \
    --node-config 'dataset-path="datasets/cifar10_part_1"' \
    --clientappio-api-address="0.0.0.0:9094"

In yet another new terminal window, start the second long-running Flower client:

flower-supernode \
    --root-certificates certificates/ca.crt \
    --auth-supernode-private-key keys/client_credentials_2 \
    --auth-supernode-public-key keys/client_credentials_2.pub \
    --node-config 'dataset-path="datasets/cifar10_part_2"' \
    --clientappio-api-address="0.0.0.0:9095"

If you generated more than 2 client credentials, you can add more clients by opening new terminal windows and running the command above. Don’t forget to specify the correct client private and public keys for each client instance you created.

[!TIP] Note the --node-config passed when spawning the SuperNode is accessible to the ClientApp via the context argument, i.e., context.node_config. In this example, the ClientApp uses it to load the dataset and then proceed with the training of the model.

@app.train()
def train(msg: Message, context: Context):
    # Read the node_config to know where dataset is located
    dataset_path = context.node_config["dataset-path"]
    # then load the dataset

Run the Flower App

With both the long-running server (SuperLink) and two SuperNodes up and running, we can now start the run. Note that the command below points to a federation named my-federation. Its entry point is defined in the pyproject.toml. You can optionally use the --stream flag to stream logs from your ServerApp running on SuperLink.

flwr run . my-federation --stream