# P2PFL - Federated Learning over P2P networks
[](https://github.com/p2pfl/p2pfl/blob/main/LICENSE.md)
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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](https://p2pfl.github.io/p2pfl/).
| Feature | Description |
|-------------------|--------------------------------------------------|
| 🚀 Easy to Use | [Get started](https://p2pfl.github.io/p2pfl/quickstart.html) 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](https://p2pfl.com). |
| 🧠 ML Frameworks | Seamlessly integrate [PyTorch](https://pytorch.org/), [TensorFlow/Keras](https://www.tensorflow.org/), and [JAX](https://github.com/google/jax) models. |
| 📡 Communication Protocol Agnostic | Choose the communication protocol that best suits your needs (e.g., [gRPC](https://grpc.io/)). |
| 🔌 Integrations | Enhanced capabilities through integrations: [Hugging Face Datasets](https://huggingface.co/datasets), ML frameworks, communication protocols, and [Ray](https://www.ray.io/) for large-scale simulations. |
## 📥 Installation
### 👨🏼💻 For Users
```bash
pip install "p2pfl[torch]"
```
### 👨🏼🔧 For Developers
[](https://codespaces.new/p2pfl/p2pfl/tree/develop?quickstart=1)
#### 🐍 Python (using Poetry)
```bash
git clone https://github.com/p2pfl/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
```bash
docker build -t p2pfl .
docker run -it --rm p2pfl bash
```
## 🎬 Quickstart
To start using P2PFL, follow our [quickstart guide](https://p2pfl.github.io/p2pfl/quickstart.html) in the documentation.
## 📚 Documentation & Resources
* **Documentation:** [https://p2pfl.github.io/p2pfl/](https://p2pfl.github.io/p2pfl)
* **Technical Report:** (first version) [other/memoria.pdf](other/memoria.pdf)
## 🤝 Contributing
We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. Please adhere to the project's code of conduct in [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md).
## 💬 Community
Connect with us and stay updated:
* [**GitHub Issues:**](https://github.com/p2pfl/p2pfl/issues) - For reporting bugs and requesting features.
* [**Google Group:**](https://groups.google.com/g/p2pfl) - For discussions and announcements.
* [**Slack:**](https://join.slack.com/t/p2pfl/shared_invite/zt-2lbqvfeqt-FkutD1LCZ86yK5tP3Duztw) - 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.
[](https://star-history.com/#p2pfl/p2pfl&Date)
## 📜 License
[GNU General Public License, Version 3.0](https://www.gnu.org/licenses/gpl-3.0.en.html)
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"description": "\n\n# P2PFL - Federated Learning over P2P networks\n\n[](https://github.com/p2pfl/p2pfl/blob/main/LICENSE.md)\n[](https://github.com/p2pfl/p2pfl/issues)\n\n\n\n\n[](https://htmlpreview.github.io/?https://github.com/p2pfl/p2pfl/blob/python-coverage-comment-action-data/htmlcov/index.html)\n[](https://join.slack.com/t/p2pfl/shared_invite/zt-2lbqvfeqt-FkutD1LCZ86yK5tP3Duztw)\n\nP2PFL 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.\n\n## \u2728 Key Features\n\nP2PFL offers a range of features designed to make decentralized federated learning accessible and efficient. For detailed information, please refer to our [documentation](https://p2pfl.github.io/p2pfl/).\n\n| Feature | Description |\n|-------------------|--------------------------------------------------|\n| \ud83d\ude80 Easy to Use | [Get started](https://p2pfl.github.io/p2pfl/quickstart.html) quickly with our intuitive API. |\n| \ud83d\udee1\ufe0f Reliable | Built for fault tolerance and resilience. |\n| \ud83c\udf10 Scalable | Leverages the power of peer-to-peer networks. |\n| \ud83e\uddea Versatile | Experiment in simulated or real-world environments.|\n| \ud83d\udd12 Private | Prioritizes data privacy with decentralized architecture.|\n| \ud83e\udde9 Flexible | Designed to be easy to modify.|\n| \ud83d\udcc8 Real-time Monitoring | Manage and track experiment through [P2PFL Web Services platform](https://p2pfl.com). |\n| \ud83e\udde0 ML Frameworks | Seamlessly integrate [PyTorch](https://pytorch.org/), [TensorFlow/Keras](https://www.tensorflow.org/), and [JAX](https://github.com/google/jax) models. |\n| \ud83d\udce1 Communication Protocol Agnostic | Choose the communication protocol that best suits your needs (e.g., [gRPC](https://grpc.io/)). |\n| \ud83d\udd0c Integrations | Enhanced capabilities through integrations: [Hugging Face Datasets](https://huggingface.co/datasets), ML frameworks, communication protocols, and [Ray](https://www.ray.io/) for large-scale simulations. |\n\n## \ud83d\udce5 Installation\n\n### \ud83d\udc68\ud83c\udffc\u200d\ud83d\udcbb For Users\n\n```bash\npip install \"p2pfl[torch]\"\n```\n\n### \ud83d\udc68\ud83c\udffc\u200d\ud83d\udd27 For Developers\n\n[](https://codespaces.new/p2pfl/p2pfl/tree/develop?quickstart=1)\n\n#### \ud83d\udc0d Python (using Poetry)\n\n```bash\ngit clone https://github.com/p2pfl/p2pfl.git\ncd p2pfl\npoetry install -E torch \n```\n\n> **Note:** Use the extras (`-E`) flag to install specific dependencies (e.g., `-E torch`). Use `--no-dev` to exclude development dependencies.\n\n#### \ud83d\udc33 Docker\n\n```bash\ndocker build -t p2pfl .\ndocker run -it --rm p2pfl bash\n```\n\n## \ud83c\udfac Quickstart\n\nTo start using P2PFL, follow our [quickstart guide](https://p2pfl.github.io/p2pfl/quickstart.html) in the documentation.\n\n## \ud83d\udcda Documentation & Resources\n\n* **Documentation:** [https://p2pfl.github.io/p2pfl/](https://p2pfl.github.io/p2pfl)\n* **Technical Report:** (first version) [other/memoria.pdf](other/memoria.pdf)\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. Please adhere to the project's code of conduct in [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md).\n\n## \ud83d\udcac Community\n\nConnect with us and stay updated:\n\n* [**GitHub Issues:**](https://github.com/p2pfl/p2pfl/issues) - For reporting bugs and requesting features.\n* [**Google Group:**](https://groups.google.com/g/p2pfl) - For discussions and announcements.\n* [**Slack:**](https://join.slack.com/t/p2pfl/shared_invite/zt-2lbqvfeqt-FkutD1LCZ86yK5tP3Duztw) - For real-time conversations and support.\n\n## \u2b50 Star History\n\nA big thank you to the community for your interest in P2PFL! We appreciate your support and contributions.\n\n[](https://star-history.com/#p2pfl/p2pfl&Date)\n\n## \ud83d\udcdc License\n\n[GNU General Public License, Version 3.0](https://www.gnu.org/licenses/gpl-3.0.en.html)\n\n",
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