Federated Learning is a machine learning paradigm for decentralized training over different clients. The training happens in rounds where each client learns a specific model which is then aggregated by a central server and passed back to the clients. Since the paradigm’s inception, many frameworks that provide Federated Learning tools and infrastructure have appeared. This leads to the question of ”How do different Federated Learning frameworks compare?”, which is the research question of this paper. The paper’s main contribution will be helping developers new to the Federated Learning field decide between NVidia Flare, OpenFL, and Flower, three popular federated learning frameworks.