STRUCTURING FEDERATED LEARNING APPLICATIONS – A LITERATURE ANALYSIS AND TAXONOMY

Authors

Philip Karnebogen, FIM Research Center, Branch Business & Information Systems Engineering of the Fraunhofer FITFollow
Can Kaymakci, Institute for Energy Efficiency in Production EEP, University of StuttgartFollow
Lukas Willburger, FIM Research Center, Branch Business & Information Systems Engineering of the Fraunhofer FITFollow
Björn Häckel, FIM Research Center, Technical University of Applied Sciences AugsburgFollow
Alexander Sauer, Fraunhofer Institute for Manufacturing Engineering and Automation IPAFollow

Abstract

Ensuring data privacy is an essential objective competing with the ever-rising capabilities of machine learning approaches fueled by vast amounts of centralized data. Federated learning addresses this conflict by moving the model to the data while ensuring that the data itself does not leave a client's device. However, maintaining privacy impels new challenges concerning algorithm performance or fairness of the algorithm's results that remain uncovered from a sociotechnical perspective. We tackle this research gap by conducting a structured literature review and analyzing 152 articles to develop a taxonomy of federated learning applications consisting of nine dimensions and 25 characteristics. Our taxonomy illustrates how different attributes of federated learning affect trade-offs between an algorithm's privacy, performance, and fairness. Despite an increasing interest in the technical implementation of federated learning, our work is one of the first to emphasize an information systems perspective on this emerging and promising topic.

Recommended Citation

Karnebogen, Philip; Kaymakci, Can; Willburger, Lukas; Häckel, Björn; and Sauer, Alexander, "STRUCTURING FEDERATED LEARNING APPLICATIONS – A LITERATURE ANALYSIS AND TAXONOMY" (2023). ECIS 2023 Research Papers. 230.
https://aisel.aisnet.org/ecis2023_rp/230