<div class="csl-bib-body">
<div class="csl-entry">Ungersbock, M., Hiessl, T., Schall, D., & Michahelles, F. (2023). Explainable Federated Learning: A Lifecycle Dashboard for Industrial Settings. <i>IEEE Pervasive Computing</i>, <i>22</i>(1), 19–28. https://doi.org/10.1109/MPRV.2022.3229166</div>
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dc.identifier.issn
1536-1268
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/190024
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dc.description.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.
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dc.language.iso
en
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dc.publisher
IEEE COMPUTER SOC
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dc.relation.ispartof
IEEE Pervasive Computing
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dc.subject
Data models
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dc.subject
Servers
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dc.subject
Federated learning
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dc.subject
Stakeholders
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dc.subject
Data visualization
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dc.subject
Training data
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dc.title
Explainable Federated Learning: A Lifecycle Dashboard for Industrial Settings
