# 🚀 NanoFed

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**NanoFed**: *Simplifying the development of privacy-preserving distributed ML models.*

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## 🌍 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.    |


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## 📦 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)

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## ✨ 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).
