Federated Learning with Tensorflow/Keras and Flower (Quickstart Example)ΒΆ
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 projectΒΆ
Clone the projectΒΆ
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
βββ tfexample
β βββ __init__.py
β βββ client_app.py # Defines your ClientApp
β βββ server_app.py # Defines your ServerApp
β βββ task.py # Defines your model, training and data loading
βββ pyproject.toml # Project metadata like dependencies and configs
βββ README.md
Install dependencies and projectΒΆ
Install the dependencies defined in pyproject.toml
as well as the tfhexample
package.
pip install -e .
Run the projectΒΆ
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 EngineΒΆ
[!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=0.05"
[!TIP] For a more detailed walk-through check our quickstart TensorFlow tutorial
Run with the Deployment EngineΒΆ
Follow this how-to guide to run the same app in this example but with Flowerβs 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.