p2pfl 0.3.0

A p2p federated learning framework

P2PFL - Federated Learning over P2P networks

P2PFL is a general-purpose open-source library for the execution (simulated and in real environments) of Decentralized Federated Learning systems, specifically making use of P2P networks and the Gossip protocol.

✨ Key Features

P2PFL offers a range of features designed to make decentralized federated learning accessible and efficient. For detailed information, please refer to our documentation.

Feature	Description
🚀 Easy to Use	Get started quickly with our intuitive API.
🛡️ Reliable	Built for fault tolerance and resilience.
🌐 Scalable	Leverages the power of peer-to-peer networks.
🧪 Versatile	Experiment in simulated or real-world environments.
🔒 Private	Prioritizes data privacy with decentralized architecture.
🧩 Flexible	Integrate with PyTorch and TensorFlow (coming soon!).
📈 Real-time Monitoring	Manage and track experiment through P2PFL Web Services.
🧠 Model Agnostic	Use any machine learning model you prefer (e.g., PyTorch models).
📡 Communication Protocol Agnostic	Choose the communication protocol that best suits your needs (e.g., gRPC).

📥 Installation

Note: We recommend using Python 3.9 or lower. We have found some compatibility issues with Python 3.10 and PyTorch.

👨🏼‍💻 For Users

pip install p2pfl

👨🏼‍🔧 For Developers

🐍 Python (using Poetry)

git clone https://github.com/pguijas/p2pfl.git
cd p2pfl
poetry install -E torch 

Note: Use the extras (-E) flag to install specific dependencies (e.g., -E torch). Use --no-dev to exclude development dependencies.

🐳 Docker

docker build -t p2pfl .
docker run -it --rm p2pfl bash

🎬 Quickstart

To start using P2PFL, follow our quickstart guide in the documentation.

📚 Documentation & Resources

• Documentation: https://pguijas.github.io/p2pfl/
• End-of-Degree Project Report: other/memoria.pdf
• Open Source Project Award Report: other/memoria-open-source.pdf

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines. Please adhere to the project's code of conduct in CODE_OF_CONDUCT.md.

💬 Community

Connect with us and stay updated:

• GitHub Discussions: - For general discussions, questions, and ideas.
• GitHub Issues: - For reporting bugs and requesting features.
• Google Group: - For discussions and announcements.
• Slack: - For real-time conversations and support.

⭐ Star History

A big thank you to the community for your interest in P2PFL! We appreciate your support and contributions.

📜 License

GNU General Public License, Version 3.0