nanofed 0.1.2

A lightweight federated learning library

🚀 NanoFed

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

pip install nanofed

Development Installation

git clone https://github.com/camille-004/nanofed.git
cd nanofed
make install

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📖 Documentation

📚 Learn how to use NanoFed in our guides and API references. 👉 Read the Docs

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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.

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🔧 Quick Start

Train a model using federated learning in just a few lines of code:

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"))

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🛠️ Getting Help

Need assistance? Here are some helpful resources:

Resource	Description
📚 Documentation	Learn how to use NanoFed effectively.
🐛 Issue Tracker	Report bugs or request features.
🛠️ Source Code	Browse the NanoFed repository on GitHub.

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⚖️ License

NanoFed is licensed under the GNU General Public License (GPL-3.0). See the LICENSE file for details.

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👩‍💻 Contributing

Contributions are welcome! We follow the Conventional Commits specification. See our contribution guidelines for detailed instructions.

Example commit message:

feat(client): add retry mechanism

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🛠️ 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

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🚀 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

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Made with ❤️ and 🧠 by Camille Dunning.