p2pfl 0.4.4

A p2p federated learning framework

P2PFL - Federated Learning over P2P networks

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

✨ 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	Designed to be easy to modify.
📈 Real-time Monitoring	Manage and track experiment through P2PFL Web Services platform.
🧠 ML Frameworks	Seamlessly integrate PyTorch, TensorFlow/Keras, and JAX models.
📡 Communication Protocol Agnostic	Choose the communication protocol that best suits your needs (e.g., gRPC).
🔌 Integrations	Enhanced capabilities through integrations: Hugging Face Datasets, ML frameworks, communication protocols, and Ray for large-scale simulations.

📥 Installation

👨🏼‍💻 For Users

pip install "p2pfl[torch]"

👨🏼‍🔧 For Developers

🐍 Python (using UV)

git clone https://github.com/p2pfl/p2pfl.git
cd p2pfl
uv sync --all-extras

Note: The above command installs all dependencies (PyTorch, TensorFlow, and Ray). If you only need specific frameworks, you can use:

• uv sync - Install only core dependencies
• uv sync --extra torch - Install with PyTorch support
• uv sync --extra tensorflow - Install with TensorFlow support
• uv sync --extra ray - Install with Ray support

Use --no-dev to exclude development dependencies.

⚠️ Important for Ray users: If you're using Ray, we recommend activating the virtual environment traditionally instead of using uv run to avoid dependency issues with Ray workers. See the installation guide for details.

🐳 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://p2pfl.github.io/p2pfl/
• Technical Report: (first version) other/memoria.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 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