Federated Learning with Tensorflow/Keras and Flower (Quickstart Example)#
This introductory example to Flower uses Tensorflow/Keras but deep knowledge of this frameworks is required to run the example. However, it will help you understand how to adapt Flower to your use case. Running this example in itself is quite easy. This example uses Flower Datasets to download, partition and preprocess the CIFAR-10 dataset.
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/quickstart-tensorflow . \
&& rm -rf _tmp \
&& cd quickstart-tensorflow
This will create a new directory called quickstart-tensorflow
with the following structure:
quickstart-tensorflow
โโโ tfexample
โ โโโ __init__.py
โ โโโ client_app.py # Defines your ClientApp
โ โโโ server_app.py # Defines your ServerApp
โ โโโ task.py # Defines your model, training and data loading
โโโ pyproject.toml # Project metadata like dependencies and configs
โโโ README.md
Install dependencies and project#
Install the dependencies defined in pyproject.toml
as well as the tfhexample
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#
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=0.05"
[!TIP] For a more detailed walk-through check our quickstart TensorFlow tutorial
Run with the Deployment Engine#
[!NOTE] An update to this example will show how to run this Flower application with the Deployment Engine and TLS certificates, or with Docker.