Flower is a federated AI framework that enables training machine learning models across decentralized data sources without exchanging raw data. It supports multiple ML frameworks including PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, and scikit-learn. The framework is designed to be customizable and extendable, suitable for both research and production use cases involving privacy-preserving distributed learning.