Flower Labs
Features
- Federated Learning Framework — Flower simplifies the deployment of federated learning models across various platforms and devices, making it accessible to all.
- Framework Agnostic — It supports popular ML frameworks like TensorFlow, PyTorch, and NumPy, ensuring compatibility with your preferred tools.
- Minimal Code Setup — You can set up a federated learning system with just 20 lines of Python code, reducing complexity and saving time.
- Scalability — Flower is designed to support real-world systems with tens of millions of clients, making it suitable for both research and production.
- Comprehensive Documentation — With extensive guides and tutorials, Flower is user-friendly for both newcomers and experienced developers.
- Platform Independence — Deploy across cloud, mobile, and edge devices without significant engineering effort, enhancing versatility.
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