  Federated machine learning has great promise to overcome the input privacy
challenge in machine learning. The appearance of several projects capable of
simulating federated learning has led to a corresponding rapid progress on
algorithmic aspects of the problem. However, there is still a lack of federated
machine learning frameworks that focus on fundamental aspects such as
scalability, robustness, security, and performance in a geographically
distributed setting. To bridge this gap we have designed and developed the FEDn
framework. A main feature of FEDn is to support both cross-device and
cross-silo training settings. This makes FEDn a powerful tool for researching a
wide range of machine learning applications in a realistic setting.
