This project is about rainfall prediction in Australia. It is based on the Flower framework with modifications to save the global model after each iteration and evaluate the trained global model.

The dataset used in this project is sourced from Kaggle and consists of Australian rainfall data, comprising approximately 140,000 records.

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/A5he1ter/AUC-Weather-Prediction.git && cd AUC-Weather-Prediction

Project dependencies (such as torch 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

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:

python3 server.py

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

Start client 1 in the first terminal:

python3 client.py --partition-id 0

Start client 2 in the second terminal:

python3 client.py --partition-id 1

Start client 3 in the third terminal:

python3 client.py --partition-id 2

If you're using a Linux or Unix-like system, you can directly execute the run.sh file.

Thanks to the authors of the Flower framework and to the providers of the Australian rainfall data.