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LEE Chul Min

Main Referenced Co-authors
DELGADO FERNANDEZ, Joaquin  (2)
FRIDGEN, Gilbert  (2)
POTENCIANO MENCI, Sergio  (2)
RIEGER, Alexander  (2)
Main Referenced Keywords
artificial intelligence (1); credit risk assessment (1); Deep neural networks (1); Differential privacy (1); Federated learning (1);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations (2)
ULHPC - University of Luxembourg: High Performance Computing (2)
Main Referenced Disciplines
Management information systems (2)
Computer science (2)
Finance (1)
Energy (1)
Engineering, computing & technology: Multidisciplinary, general & others (1)

Publications (total 2)

The most downloaded
407 downloads
Delgado Fernandez, J., Potenciano Menci, S., Lee, C. M., Rieger, A., & Fridgen, G. (15 November 2022). Privacy-preserving federated learning for residential short-term load forecasting. Applied Energy, 326. doi:10.1016/j.apenergy.2022.119915 https://hdl.handle.net/10993/52125

The most cited

23 citations (WOS)

Delgado Fernandez, J., Potenciano Menci, S., Lee, C. M., Rieger, A., & Fridgen, G. (15 November 2022). Privacy-preserving federated learning for residential short-term load forecasting. Applied Energy, 326. doi:10.1016/j.apenergy.2022.119915 https://hdl.handle.net/10993/52125

Lee, C. M., Delgado Fernandez, J., Potenciano Menci, S., Rieger, A., & Fridgen, G. (03 January 2023). Federated Learning for Credit Risk Assessment [Paper presentation]. Proceedings of the 56th Hawaii International Conference on System Sciences, Maui, Hawaii, United States.
Peer reviewed

Delgado Fernandez, J., Potenciano Menci, S., Lee, C. M., Rieger, A., & Fridgen, G. (15 November 2022). Privacy-preserving federated learning for residential short-term load forecasting. Applied Energy, 326. doi:10.1016/j.apenergy.2022.119915
Peer Reviewed verified by ORBi

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