Structuring Federated Learning Applications : A Literature Analysis and Taxonomy

Title data

Karnebogen, Philip ; Kaymakci, Can ; Willburger, Lukas ; Häckel, Björn ; Sauer, Alexander:
Structuring Federated Learning Applications : A Literature Analysis and Taxonomy.
2023
Event: Proceedings of the 31th European Conference on Information Systems (ECIS) , 11.-16.06.2023 , Kristiansand, Norway.
(Conference item: Conference , Speech )

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Abstract in another language

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 and ensuring 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 with nine dimensions and 24 characteristics. Our taxonomy illustrates
how different attributes of federated learning may affect the trade-off 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.