Machine Learning (ML) and Artificial Intelligence (AI) have shown promising\nresults in many areas and are driven by the increasing amount of available\ndata. However, this data is often distributed across different institutions and\ncannot be shared due to privacy concerns. Privacy-preserving methods, such as\nFederated Learning (FL), allow for training ML models without sharing sensitive\ndata, but their implementation is time-consuming and requires advanced\nprogramming skills. Here, we present the FeatureCloud AI Store for FL as an\nall-in-one platform for biomedical research and other applications. It removes\nlarge parts of this complexity for developers and end-users by providing an\nextensible AI Store with a collection of ready-to-use apps. We show that the\nfederated apps produce similar results to centralized ML, scale well for a\ntypical number of collaborators and can be combined with Secure Multiparty\nComputation (SMPC), thereby making FL algorithms safely and easily applicable\nin biomedical and clinical environments.\n