Recent developments in the field of federated learning indicate the feasibility of implementing machine learning systems in which user data remains private. Moreover, in decentralized learning, no central aggregation server is needed. However, regarding the real-world application of such architectures in the medical domain many questions remain, especially regarding the security requirements for medical care systems.