1. The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond
- Author
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Matschinske, Julian, Sp��th, Julian, Nasirigerdeh, Reza, Torkzadehmahani, Reihaneh, Hartebrodt, Anne, Orb��n, Bal��zs, Fej��r, S��ndor, Zolotareva, Olga, Bakhtiari, Mohammad, Bihari, B��la, Bloice, Marcus, Donner, Nina C, Fdhila, Walid, Frisch, Tobias, Hauschild, Anne-Christin, Heider, Dominik, Holzinger, Andreas, H��tzendorfer, Walter, Hospes, Jan, Kacprowski, Tim, Kastelitz, Markus, List, Markus, Mayer, Rudolf, Moga, M��nika, M��ller, Heimo, Pustozerova, Anastasia, R��ttger, Richard, Saranti, Anna, Schmidt, Harald HHW, Tschohl, Christof, Wenke, Nina K, and Baumbach, Jan
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Science - Cryptography and Security ,Computer Science - Distributed, Parallel, and Cluster Computing ,Distributed, Parallel, and Cluster Computing (cs.DC) ,Cryptography and Security (cs.CR) ,Machine Learning (cs.LG) - Abstract
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.
- Published
- 2021