Flower TabNet Example using TensorFlow#

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This introductory example to Flower uses Keras but deep knowledge of Keras is not necessarily required to run the example. However, it will help you understanding how to adapt Flower to your use-cases. You can learn more about TabNet from paper and its implementation using TensorFlow at this repository. Note also that the basis of this example using federated learning is the example from the repository above.

Project Setup#

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:

git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/quickstart-tabnet . && rm -rf flower && cd quickstart-tabnet

This will create a new directory called quickstart-tabnet containing the following files:

-- pyproject.toml
-- requirements.txt
-- client.py
-- server.py
-- README.md

Installing Dependencies#

Project dependencies (such as tensorflow and flwr) are defined in pyproject.toml and requirements.txt. We recommend Poetry to install those dependencies and manage your virtual environment (Poetry installation) or pip, but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.


poetry install
poetry shell

Poetry will install all your dependencies in a newly created virtual environment. To verify that everything works correctly you can run the following command:

poetry run python3 -c "import flwr"

If you don’t see any errors you’re good to go!


Write the command below in your terminal to install the dependencies according to the configuration file requirements.txt.

pip install -r requirements.txt

Run Federated Learning with TensorFlow/Keras and Flower#

Afterwards you are ready to start the Flower server as well as the clients. You can simply start the server in a terminal as follows:

poetry run python server.py

Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two more terminals and run the following command in each:

poetry run python client.py

Alternatively you can run all of it in one shell as follows:

poetry run python server.py &
poetry run python client.py &
poetry run python client.py