Ungersbock, M., Hiessl, T., Schall, D., & Michahelles, F. (2023). Explainable Federated Learning: A Lifecycle Dashboard for Industrial Settings. IEEE Pervasive Computing, 22(1), 19–28. https://doi.org/10.1109/MPRV.2022.3229166
E193 - Institut für Visual Computing and Human-Centered Technology E193-04 - Forschungsbereich Artifact-based Computing & User Research
-
Journal:
IEEE Pervasive Computing
-
ISSN:
1536-1268
-
Date (published):
1-Jan-2023
-
Number of Pages:
10
-
Publisher:
IEEE COMPUTER SOC
-
Peer reviewed:
Yes
-
Keywords:
Data models; Servers; Federated learning; Stakeholders; Data visualization; Training data
en
Abstract:
As the adoption of federated learning (FL) in the manufacturing industry grows and systems get increasingly complex, a need to inspect their behavior arises. Stakeholders of the FL process want a more transparent system to understand the current state and analyze how its performance changed over time. However, current representation approaches are often not designed for industrial applications and do not cover the entire FL model lifecycle. We propose the lifecycle dashboard, which considers the different requirements and perspectives of industrial stakeholders by visualizing information from the FL server. In addition, our representation approach is generic enough to be applied to different use cases and industries. We evaluate the lifecycle dashboard in a semistructured expert interview, show improvements in the understandability of FL systems, and discuss possible use cases in the industry.
