--- tags: [quickstart, tabular, fds] dataset: [Iris] framework: [scikit-learn] --- # Federated Learning with scikit-learn and Flower (Quickstart Example) [View on GitHub](https://github.com/adap/flower/blob/main/examples/quickstart-sklearn-tabular) This example of Flower uses `scikit-learn`'s `LogisticRegression` model to train a federated learning system on "iris" (tabular) dataset. It will help you understand how to adapt Flower for use with `scikit-learn`. Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the dataset. ## Set up the project ### Clone the project 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: ```shell git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/quickstart-sklearn-tabular . && rm -rf flower && cd quickstart-sklearn-tabular ``` This will create a new directory called `quickstart-sklearn-tabular` with the following structure: ```shell quickstart-sklearn-tabular ├── sklearnexample │ ├── __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 └── README.md ``` ### Install dependencies and project Install the dependencies defined in `pyproject.toml` as well as the `sklearnexample` 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 penalty="'l1'" ``` ### 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.