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- Getting Started
- What is Federated Learning?
- What is Flower Framework?
- Contribution
- License
- References
Welcome to Flower4all, a tutorial series repository dedicated to exploring the Flower framework for federated learning. This repository contains several Python flower projects utilizing libraries such as Pandas, NumPy, and leveraging one of the flower datasets, MNIST.
To get started with Flower4all tutorials, navigate to the project of your interest within this repository. Each tutorial typically includes at least one client.py and one server.py.
In Federated Learning, the server should be run before client.py. Please ensure you run the server script first before the client script to avoid any issues.
Federated Learning is a machine learning approach that allows for training on decentralized data sources, such as mobile devices, without the need to directly access raw data. Instead, model training occurs locally on each device, and only model updates are shared with a central server or aggregator. This preserves data privacy and reduces communication overhead.
Flower (Federated Learning with Multi-task Privacy) is an open-source Python framework for federated learning. It simplifies the process of building federated learning systems by providing high-level abstractions and utilities for communication, aggregation, and orchestration of federated learning tasks.
Contributions to Flower4all are welcome! If you find any bugs, have feature requests, or want to contribute enhancements, please feel free to open an issue or submit a pull request.
- Fork the repository.
- Create your feature branch (
git checkout -b feature/YourFeature). - Commit your changes (
git commit -am 'Add some feature'). - Push to the branch (
git push origin feature/YourFeature). - Open a pull request.
This project is licensed under the MIT License.
- Flower Framework: Official Documentation
- Federated Learning: Research Paper