--- tags: [quickstart, tabular, federated analytics] dataset: [Iris] framework: [pandas] --- # Federated Learning with Pandas and Flower (Quickstart Example) [View on GitHub](https://github.com/adap/flower/blob/main/examples/quickstart-pandas) > \[!CAUTION\] > This example uses Flower's low-level API which remains a preview feature and subject to change. Both `ClientApp` and `ServerApp` operate directly on [Message](https://flower.ai/docs/framework/ref-api/flwr.common.Message.html) and [RecordSet](https://flower.ai/docs/framework/ref-api/flwr.common.RecordSet.html) objects. This introductory example to Flower uses [Pandas](https://pandas.pydata.org/), but deep knowledge of Pandas is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the [Iris dataset](https://huggingface.co/datasets/scikit-learn/iris). Running this example in itself is quite easy. This example implements a form of Federated Analyics by which instead of training a model using locally available data, the nodes run a query on the data they own. In this example the query is to compute the histogram on specific columns of the dataset. These metrics are sent to the `ServerApp` for aggregation. ## 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/quickstart-pandas . \ && rm -rf _tmp && cd quickstart-pandas ``` This will create a new directory called `quickstart-pandas` with the following structure: ```shell quickstart-pandas ├── pandas_example │ ├── __init__.py │ ├── client_app.py # Defines your ClientApp │ └── server_app.py # Defines your ServerApp ├── 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 `pandas_example` 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 ``` > \[!TIP\] > For a more detailed walk-through check our [quickstart PyTorch tutorial](https://flower.ai/docs/framework/tutorial-quickstart-pandas.html) ### 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.