Federated learning is a distributed machine learning approach that enables the training of models without the need to share data. This project is a simple and easy-to-use federated learning framework that can be integrated with popular machine learning frameworks such as PyTorch, TensorFlow, JAX, and scikit-learn. It supports federated learning training, analysis, and evaluation, as well as the simulation of client-side operations. The framework includes a wealth of examples and is suitable for the development of privacy-preserving machine learning models in scenarios that require data protection, such as healthcare, government and corporate enterprises, and finance.