MXNet: From Centralized To Federated#

This example demonstrates how an already existing centralized MXNet-based machine learning project can be federated with Flower.

This introductory example for Flower uses MXNet, but you’re not required to be a MXNet expert to run the example. The example will help you to understand how Flower can be used to build federated learning use cases based on an existing MXNet project.

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 && mv flower/examples/mxnet-from-centralized-to-federated . && rm -rf flower && cd mxnet-from-centralized-to-federated

This will create a new directory called mxnet-from-centralized-to-federated containing the following files:

-- pyproject.toml
-- requirements.txt

Installing Dependencies#

Project dependencies (such as mxnet 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 MXNet Federated#

This MXNet example is based on the Handwritten Digit Recognition tutorial and uses the MNIST dataset (hand-written digits with 28x28 pixels in greyscale with 10 classes). Feel free to consult the tutorial if you want to get a better understanding of MXNet. The file contains all the steps that are described in the tutorial. It loads the dataset and a sequential model, trains the model with the training set, and evaluates the trained model on the test set.

The only things we need are a simple Flower server (in and a Flower client (in The Flower client basically takes model and training code tells Flower how to call it.

Start the server in a terminal as follows:


Now that the server is running and waiting for clients, we can start two clients that will participate in the federated learning process. To do so simply open two more terminal windows and run the following commands.

Start client 1 in the first terminal:


Start client 2 in the second terminal:


You are now training a MXNet-based classifier on MNIST, federated across two clients. The setup is of course simplified since both clients hold the same dataset, but you can now continue with your own explorations. How about changing from a sequential model to a CNN? How about adding more clients?