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Explainable Federated Learning: A Lifecycle Dashboard for Industrial Settings
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  • Michael Ungersböck ,
  • Thomas Hiessl ,
  • Daniel Schall ,
  • Florian Michahelles
Michael Ungersböck
Siemens Technology and TU Wien

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Thomas Hiessl
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Daniel Schall
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Florian Michahelles
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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 semi-structured expert interview, show improvements in the understandability of FL systems, and discuss possible use cases in the industry.
01 Jan 2023Published in IEEE Pervasive Computing volume 22 issue 1 on pages 19-28. 10.1109/MPRV.2022.3229166