Thanks for the question, very natural to ask. We are also fans of PySft. It offers support for a very wide range of privacy enhancing machine learning tools. But where Flower and PySft differ is in focus. Federated learning is difficult and requires many technical moving parts all working together (e.g., secure aggregation, differential privacy, scalable simulation, device deployments, integration with conventional ML frameworks etc.). All of these need to tightly integrated, and in a manner that performs federated learning efficiently. This is where Flower currently excels. It offers comprehensive, extensible and, most important, easy to use construction of federations that need these different parts together. We believe it offers the best user experience for federated learning currently out there. We hope in the future many tool suites that offer private machine learning (like PySft and others) will actually adopt Flower components so we can all work better together.