Federated Learning Experiments

The repository accompanies our publication in the Data and Policy Journal titled "Overcoming Intergovernmental Data Sharing Challenges with Federated Learning". The repository contains a series of experiments around federated learning, primarily leveraging BERT for multi-class text classification tasks. Our main script run.sh runs a set of experiments: centralized learning, per-country learning, federated learning, and privacy-preserving federated learning. Please contact the repo owner to obtain the used dataset, as we do not share it due to privacy and ethical reasons.

Installation

1. Clone the repository

git clone https://github.com/sprenkamp/federated_learning_data_and_policy.git
cd federated_learning_data_and_policy

2. Create a Python virtual environment and activate it (optional but recommended)

python3 -m venv venv
source venv/bin/activate  # On Windows, use `.\venv\Scripts\activate`

3. Install the required Python packages

pip install -r requirements.txt

Running the Experiments

To run the experiments, use the run.sh script. Ensure that it has the right permissions.

bash run.sh

The run.sh script will run the following experiments in order:

1. Centralized Learning: This experiment uses BERT for multi-class text classification in a centralized manner.

python src/multi_class_text_classifier/train_bert_based_centralized.py

2. Per-country Learning: This experiment takes a look at learning BERT-based multi-class text classifiers per country.

python src/multi_class_text_classifier/train_bert_based_per_country.py

3. Federated Learning: In this experiment, we use BERT for multi-class text classification in a federated learning setup.

python src/multi_class_text_classifier/train_bert_based_federated.py

4. Privacy-Preserving Federated Learning: Lastly, this experiment adds a privacy-preserving layer to the federated learning setup.

python src/multi_class_text_classifier/train_bert_based_federated_private.py