Hiessl, ThomasThomasHiesslLakani, Safoura RezapourSafoura RezapourLakaniUngersboeck, MichaelMichaelUngersboeckKemnitz, JanaJanaKemnitzSchall, DanielDanielSchallSchulte, StefanStefanSchulte2024-02-232024-02-232023-057th IEEE International Conference on Fog and Edge Computing (ICFEC 2023)9798350322880https://hdl.handle.net/11420/45957In industrial settings, traditional centralized ap-proaches for training AI models can be insufficient due to limited training data. Industrial Federated Learning (IFL) offers a promising solution by enabling collaborative training across multiple industrial devices, while keeping data on-premises. In this paper, we propose a novel approach for supporting the development, deployment, integration and execution of IFL solutions. The proposed method provides a lifecycle management of FL artifacts and supports FL as a Service (FlaaS). This enables the extensibility and customizability of FL-based edge applications in industrial settings. Additionally, we introduce a federated clustering algorithm that we have integrated into a condition monitoring app running on client locations to evaluate the proposed lifecycle management. We run two scenarios with four and 33 clients using real-world time series data from industrial pumps. Our results show the applicability of the implemented lifecycle management and demonstrates that privacy-preserving approaches compete well with privacy-disclosing ones.enFederated LearningLifecycle ManagementMLE@TUHHConference Paper10.1109/ICFEC57925.2023.00010Conference Paper