Flower: A Friendly Federated Learning Framework
Flower is an open-source federated learning framework designed to facilitate collaboration between parties who want to train machine learning models without sharing their data with each other. Flower provides a unified approach to federated learning, analytics, and uation by enabling the federation of any workload, any machine learning framework, and any programming language.
With Flower, developers can federate any machine learning workload, including computer vision, natural language processing, and reinforcement learning, on any framework, such as TensorFlow or PyTorch, written in any programming language. The framework is easy to use and has excellent documentation and tutorials, making it an attractive option for beginners as well as advanced users.
Flower provides users with unprecedented flexibility, allowing them to develop production-ready solutions for their customers. It can handle the scaling complexities of federated learning, making it possible to scale FL experiments to even 1,000 or 10,000 clients, which is a significant challenge.
Some of the best organizations in the world have used Flower in their federated learning projects. Getting started with Flower is easy: users can refer to the code examples that demonstrate various usage scenarios of Flower in combination with popular machine learning frameworks like TensorFlow, PyTorch, and HuggingFace.
If you are interested in developing a federated learning project, then Flower is the tool you need. Its ease of use, flexibility, scalability, and compatibility with popular machine learning frameworks make it a great option for your FL project.