This repository contains the implementation of a federated learning simulation for the Federated Data Quality (FedDQ) project. The goal of FedDQ is to improve data quality in federated learning settings.
This repository provides the necessary code to simulate the FedDQ approach. It includes components for clients, a federated server, model operations using PyTorch, dataset loading, and knowledge graph pre-processing.
The repository consists of the following main components:
client.py: Contains the code for the federated learning clients. Each client participates in the training process by contributing its local model updates.server.py: Implements the federated server responsible for aggregating the model updates from the clients and updating the global model.model.py: Defines the model architecture and operations using PyTorch. This includes training, evaluation, and inference functions.dataset.py: Provides functionality for loading and preprocessing the dataset used in the federated learning simulation.utils.py: Contains utility functions for knowledge graph pre-processing and other auxiliary operations.main.py: Contains the main driver for simulation execution
To use this project, please follow these steps:
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Clone the repository:
git clone https://github.com/Ahmad-Traboulsi/FedDQ.git
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Install the required dependencies:
pip install -r requirements.txt
Note: Make sure you have Python and pip installed on your system.
To run the FedDQ simulation, follow these steps:
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Go to the project directory:
cd FedDQ -
Customize the configurations and settings in the code files according to your requirements.
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Execute the main file to launch server and clients:
python main.py
Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request. Let's collaborate to enhance the FedDQ project together!
This project is licensed under the MIT License. Feel free to use and modify the code as per the license terms.