  Machine Learning (ML) and Artificial Intelligence (AI) have shown promising
results in many areas and are driven by the increasing amount of available
data. However, this data is often distributed across different institutions and
cannot be shared due to privacy concerns. Privacy-preserving methods, such as
Federated Learning (FL), allow for training ML models without sharing sensitive
data, but their implementation is time-consuming and requires advanced
programming skills. Here, we present the FeatureCloud AI Store for FL as an
all-in-one platform for biomedical research and other applications. It removes
large parts of this complexity for developers and end-users by providing an
extensible AI Store with a collection of ready-to-use apps. We show that the
federated apps produce similar results to centralized ML, scale well for a
typical number of collaborators and can be combined with Secure Multiparty
Computation (SMPC), thereby making FL algorithms safely and easily applicable
in biomedical and clinical environments.
