Flower with MLflow for Custom Dataset

Uses https://github.com/adap/flower/tree/main/examples/quickstart-pytorch for base implementation

Uses https://github.com/nimbus-gateway/cords-semantics-lib/tree/main for MLflow tagging

Installing Dependencies

Project dependencies (such as torch and flwr) are defined in pyproject.toml. You can install the dependencies by invoking pip:

# From a new python environment, run:
pip install .

Then, to verify that everything works correctly you can run the following command:

python -c "import flwr"

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

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Run Federated Learning with MLflow, PyTorch and Flower

Start mlflow local server using:

mlflow UI

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:

python server.py

Now you are ready to start the Flower clients which will participate in the learning. We need to specify the client id to use different clients. To do so simply open two more terminal windows and run the following commands.

Start client 1 in the first terminal:

python client.py --client-id 0

Start client 2 in the second terminal:

python client.py --client-id 1

You will see that PyTorch is starting a federated training. Look at the code for a detailed explanation.

With both the long-running server (SuperLink) and two clients (SuperNode) up and running, we can now run the actual Flower App:

flower-server-app server:app --insecure