A simulation framework for federated learning experiments, allowing researchers to test and evaluate privacy-preserving machine learning algorithms on decentralized datasets.
This repository is part of the Eation5 GitHub profile transformation project, showcasing advanced AI/ML engineering skills in Python.
- Core Functionality: Detailed implementation of Federated Learning Simulation.
- Technology Stack: Built with Python and leveraging key libraries/frameworks such as federated-learning, privacy-preserving-ai, distributed-ml.
- Scalability: Designed for high performance and scalability in enterprise-level AI applications.
Clone the repository and follow the instructions in the docs/ directory to set up your environment and run the examples.
Contributions are welcome! Please refer to CONTRIBUTING.md for guidelines.
This project is licensed under the MIT License - see the LICENSE file for details.