Secure aggregation with Flower (the SecAgg+ protocol)

The following steps describe how to use Flower’s built-in Secure Aggregation components. This example demonstrates how to apply SecAgg+ to the same federated learning workload as in the quickstart-pytorch example. The ServerApp uses the SecAggPlusWorkflow while ClientApp uses the secaggplus_mod. To introduce the various steps involved in SecAgg+, this example introduces as a sub-class of SecAggPlusWorkflow the SecAggPlusWorkflowWithLogs. It is enabled by default, but you can disable (see later in this readme).

Set up the project

Clone the project

Start by cloning the example project:

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

This will create a new directory called flower-secure-aggregation containing the following files:

flower-secure-aggregation
|
├── secaggexample
|   ├── __init__.py
|   ├── client_app.py    # Defines your ClientApp
|   ├── server_app.py    # Defines your ServerApp
|   ├── task.py          # Defines your model, training and data loading
|   └── workflow_with_log.py # Defines a workflow used when `is-demo=true`
├── pyproject.toml       # Project metadata like dependencies and configs
└── README.md

Install dependencies and project

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

pip install -e .

Run the project

You can run your Flower project in both simulation and deployment mode without making changes to the code. If you are starting with Flower, we recommend you using the simulation mode as it requires fewer components to be launched manually. By default, flwr run will make use of the Simulation Engine.

Run with the Simulation Engine

[!NOTE] Check the Simulation Engine documentation to learn more about Flower simulations and how to optimize them.

flwr run .

You can also override some of the settings for your ClientApp and ServerApp defined in pyproject.toml. For example

flwr run . --run-config "num-server-rounds=5 learning-rate=0.25"

To adapt the example for a practial usage, set is-demo=false like shown below. You might want to adjust the num-shares and reconstruction-threshold settings to suit your requirements. You can override those via --run-config as well.

flwr run . --run-config is-demo=false

Run with the Deployment Engine

Follow this how-to guide to run the same app in this example but with Flower’s Deployment Engine. After that, you might be intersted in setting up secure TLS-enabled communications and SuperNode authentication in your federation.

If you are already familiar with how the Deployment Engine works, you may want to learn how to run it using Docker. Check out the Flower with Docker documentation.