--- tags: [advanced, secure_aggregation, privacy] dataset: [CIFAR-10] framework: [torch, torchvision] --- # Secure aggregation with Flower (the SecAgg+ protocol) [View on GitHub](https://github.com/adap/flower/blob/main/examples/flower-secure-aggregation) 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](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch) example. The `ServerApp` uses the [`SecAggPlusWorkflow`](https://flower.ai/docs/framework/ref-api/flwr.server.workflow.SecAggPlusWorkflow.html#secaggplusworkflow) while `ClientApp` uses the [`secaggplus_mod`](https://flower.ai/docs/framework/ref-api/flwr.client.mod.secaggplus_mod.html#flwr.client.mod.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: ```shell 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: ```shell 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. ```bash 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](https://flower.ai/docs/framework/how-to-run-simulations.html) to learn more about Flower simulations and how to optimize them. ```bash flwr run . ``` You can also override some of the settings for your `ClientApp` and `ServerApp` defined in `pyproject.toml`. For example ```bash 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. ```bash flwr run . --run-config is-demo=false ``` ### Run with the Deployment Engine Follow this [how-to guide](https://flower.ai/docs/framework/how-to-run-flower-with-deployment-engine.html) 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](https://flower.ai/docs/framework/how-to-enable-tls-connections.html) and [SuperNode authentication](https://flower.ai/docs/framework/how-to-authenticate-supernodes.html) 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](https://flower.ai/docs/framework/docker/index.html) documentation.