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.
@article{arxiv.2105.05734,
title = {The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond},
author = {Julian Matschinske and Julian Späth and Reza Nasirigerdeh and Reihaneh Torkzadehmahani and Anne Hartebrodt and Balázs Orbán and Sándor Fejér and Olga Zolotareva and Mohammad Bakhtiari and Béla Bihari and Marcus Bloice and Nina C Donner and Walid Fdhila and Tobias Frisch and Anne-Christin Hauschild and Dominik Heider and Andreas Holzinger and Walter Hötzendorfer and Jan Hospes and Tim Kacprowski and Markus Kastelitz and Markus List and Rudolf Mayer and Mónika Moga and Heimo Müller and Anastasia Pustozerova and Richard Röttger and Anna Saranti and Harald HHW Schmidt and Christof Tschohl and Nina K Wenke and Jan Baumbach},
journal= {arXiv preprint arXiv:2105.05734},
year = {2021}
}