# 🚀 NanoFed
**NanoFed**: *Simplifying the development of privacy-preserving distributed ML models.*
---
## 🌍 What is Federated Learning?
Federated Learning (FL) is a **distributed machine learning paradigm** that trains a global model across multiple clients (devices or organizations) without sharing their data. Instead, clients send model updates to a central server for aggregation.
### **Key Benefits**
| 🌟 Feature | Description |
|------------------------|--------------------------------------------------|
| 🔒 **Privacy Preservation** | Data stays securely on devices. |
| 🚀 **Resource Efficiency** | Decentralized training reduces transfer overhead.|
| 🌐 **Scalable AI** | Enables collaborative training environments. |
---
## 📦 Installation
### **Requirements**
- Python `3.10+`
- Dependencies installed automatically
### **Install with Pip**
```bash
pip install nanofed
```
### **Development Installation**
```bash
git clone https://github.com/camille-004/nanofed.git
cd nanofed
make install
```
---
## 📖 Documentation
📚 **Learn how to use NanoFed in our guides and API references.**
👉 [**Read the Docs**](https://nanofed.readthedocs.io)
---
## ✨ Key Features
- 🔒 **Privacy-First**: Keep data on devices while training.
- 🚀 **Easy-to-Use**: Simple APIs with seamless PyTorch integration.
- 🔧 **Flexible**: Customizable aggregation strategies and extensible architecture.
- 💻 **Production Ready**: Robust error handling and logging.
### **Feature Overview**
| Feature | Description |
|--------------------------|--------------------------------------------------|
| 🔒 **Privacy-First** | Data never leaves devices. |
| 🚀 **Intuitive API** | Built for developers with PyTorch support. |
| 🔧 **Flexible Aggregation** | Supports custom strategies. |
| 💻 **Production Ready** | Async communication, robust error handling. |
---
## 🔧 Quick Start
Train a model using federated learning in just a few lines of code:
```python
import asyncio
from nanofed import HTTPClient, TorchTrainer, TrainingConfig
async def run_client(client_id: str, server_url: str):
training_config = TrainingConfig(epochs=1, batch_size=256, learning_rate=0.1)
async with HTTPClient(server_url, client_id) as client:
model_state, _ = await client.fetch_global_model()
await client.submit_update(model_state)
if __name__ == "__main__":
asyncio.run(run_client("client1", "http://localhost:8080"))
```
---
## 🛠️ Getting Help
Need assistance? Here are some helpful resources:
| Resource | Description |
|------------------------|------------------------------------------------|
| 📚 **[Documentation](https://nanofed.readthedocs.io)** | Learn how to use NanoFed effectively. |
| 🐛 **[Issue Tracker](https://github.com/camille-004/nanofed/issues)** | Report bugs or request features. |
| 🛠️ **[Source Code](https://github.com/camille-004/nanofed)** | Browse the NanoFed repository on GitHub. |
---
## ⚖️ License
NanoFed is licensed under the **GNU General Public License (GPL-3.0)**.
See the [LICENSE](https://github.com/camille-004/nanofed/blob/main/LICENSE) file for details.
---
## 👩💻 Contributing
Contributions are welcome! We follow the [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/) specification. See our [contribution guidelines](https://github.com/camille-004/nanofed/blob/main/CONTRIBUTING.md) for detailed instructions.
Example commit message:
```bash
feat(client): add retry mechanism
```
---
## 🛠️ Development Roadmap
### ✅ Completed
**Core Features for V1**
- Basic client-server architecture with HTTP communication
- Simple global model management
- Basic FedAvg implementation
- Local training support
- Support for PyTorch models
- Synchronous training (all clients must complete before aggregation)
- Basic error handling and logging
---
### 🚀 Future Enhancements
**Planned Features**
- Advanced privacy features: Differential Privacy (DP), Secure Multiparty Computation (MPC), Homomorphic Encryption (HE)
- Asynchronous updates for faster and more flexible training
- Non-IID data handling for diverse client datasets
- Custom aggregation strategies for specific use cases
- gRPC implementation for high-performance communication
- Model compression techniques to reduce bandwidth usage
- Fault tolerance mechanisms for unreliable clients or servers
---
> Made with ❤️ and 🧠 by [Camille Dunning](https://github.com/camille-004).
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"description": "# \ud83d\ude80 NanoFed\n\n\n\n\n\n\n\n\n**NanoFed**: *Simplifying the development of privacy-preserving distributed ML models.*\n\n---\n\n## \ud83c\udf0d What is Federated Learning?\n\nFederated Learning (FL) is a **distributed machine learning paradigm** that trains a global model across multiple clients (devices or organizations) without sharing their data. Instead, clients send model updates to a central server for aggregation.\n\n### **Key Benefits**\n\n| \ud83c\udf1f Feature | Description |\n|------------------------|--------------------------------------------------|\n| \ud83d\udd12 **Privacy Preservation** | Data stays securely on devices. |\n| \ud83d\ude80 **Resource Efficiency** | Decentralized training reduces transfer overhead.|\n| \ud83c\udf10 **Scalable AI** | Enables collaborative training environments. |\n\n\n---\n\n## \ud83d\udce6 Installation\n\n### **Requirements**\n\n- Python `3.10+`\n- Dependencies installed automatically\n\n### **Install with Pip**\n\n```bash\npip install nanofed\n```\n\n### **Development Installation**\n\n```bash\ngit clone https://github.com/camille-004/nanofed.git\ncd nanofed\nmake install\n```\n\n---\n\n## \ud83d\udcd6 Documentation\n\n\ud83d\udcda **Learn how to use NanoFed in our guides and API references.**\n\ud83d\udc49 [**Read the Docs**](https://nanofed.readthedocs.io)\n\n---\n\n## \u2728 Key Features\n\n- \ud83d\udd12 **Privacy-First**: Keep data on devices while training.\n- \ud83d\ude80 **Easy-to-Use**: Simple APIs with seamless PyTorch integration.\n- \ud83d\udd27 **Flexible**: Customizable aggregation strategies and extensible architecture.\n- \ud83d\udcbb **Production Ready**: Robust error handling and logging.\n\n### **Feature Overview**\n\n| Feature | Description |\n|--------------------------|--------------------------------------------------|\n| \ud83d\udd12 **Privacy-First** | Data never leaves devices. |\n| \ud83d\ude80 **Intuitive API** | Built for developers with PyTorch support. |\n| \ud83d\udd27 **Flexible Aggregation** | Supports custom strategies. |\n| \ud83d\udcbb **Production Ready** | Async communication, robust error handling. |\n\n---\n\n## \ud83d\udd27 Quick Start\n\nTrain a model using federated learning in just a few lines of code:\n\n```python\nimport asyncio\nfrom nanofed import HTTPClient, TorchTrainer, TrainingConfig\n\nasync def run_client(client_id: str, server_url: str):\n training_config = TrainingConfig(epochs=1, batch_size=256, learning_rate=0.1)\n async with HTTPClient(server_url, client_id) as client:\n model_state, _ = await client.fetch_global_model()\n await client.submit_update(model_state)\n\nif __name__ == \"__main__\":\n asyncio.run(run_client(\"client1\", \"http://localhost:8080\"))\n```\n\n---\n\n## \ud83d\udee0\ufe0f Getting Help\n\nNeed assistance? Here are some helpful resources:\n\n| Resource | Description |\n|------------------------|------------------------------------------------|\n| \ud83d\udcda **[Documentation](https://nanofed.readthedocs.io)** | Learn how to use NanoFed effectively. |\n| \ud83d\udc1b **[Issue Tracker](https://github.com/camille-004/nanofed/issues)** | Report bugs or request features. |\n| \ud83d\udee0\ufe0f **[Source Code](https://github.com/camille-004/nanofed)** | Browse the NanoFed repository on GitHub. |\n\n---\n\n## \u2696\ufe0f License\n\nNanoFed is licensed under the **GNU General Public License (GPL-3.0)**.\nSee the [LICENSE](https://github.com/camille-004/nanofed/blob/main/LICENSE) file for details.\n\n---\n\n## \ud83d\udc69\u200d\ud83d\udcbb Contributing\n\nContributions are welcome! We follow the [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/) specification. See our [contribution guidelines](https://github.com/camille-004/nanofed/blob/main/CONTRIBUTING.md) for detailed instructions.\n\nExample commit message:\n```bash\nfeat(client): add retry mechanism\n```\n\n---\n\n## \ud83d\udee0\ufe0f Development Roadmap\n\n### \u2705 Completed\n**Core Features for V1**\n- Basic client-server architecture with HTTP communication\n- Simple global model management\n- Basic FedAvg implementation\n- Local training support\n- Support for PyTorch models\n- Synchronous training (all clients must complete before aggregation)\n- Basic error handling and logging\n\n---\n\n### \ud83d\ude80 Future Enhancements\n**Planned Features**\n- Advanced privacy features: Differential Privacy (DP), Secure Multiparty Computation (MPC), Homomorphic Encryption (HE)\n- Asynchronous updates for faster and more flexible training\n- Non-IID data handling for diverse client datasets\n- Custom aggregation strategies for specific use cases\n- gRPC implementation for high-performance communication\n- Model compression techniques to reduce bandwidth usage\n- Fault tolerance mechanisms for unreliable clients or servers\n\n---\n\n> Made with \u2764\ufe0f and \ud83e\udde0 by [Camille Dunning](https://github.com/camille-004).\n",
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