MIRABI, Meghdad, NIKIEL, René Klaus und BINNIG, Carsten, 2023. Safe ML: a privacy-preserving Byzantine-robust framework for distributed machine learning training. 23 rd IEEE International Conference on Data Mining Workshops. 2023. No. (2023), Seite 207-216, p. , Seite 207-216. DOI 10.1109/ICDMW60847.2023.00033.
Elsevier - Harvard (with titles)Mirabi, M., Nikiel, R.K., Binnig, C., 2023. Safe ML: a privacy-preserving Byzantine-robust framework for distributed machine learning training. 23 rd IEEE International Conference on Data Mining Workshops , Seite 207-216. https://doi.org/10.1109/ICDMW60847.2023.00033
American Psychological Association 7th editionMirabi, M., Nikiel, R. K., & Binnig, C. (ca. 2023). Safe ML: a privacy-preserving Byzantine-robust framework for distributed machine learning training [Electronic]. 23 rd IEEE International Conference on Data Mining Workshops, (2023), Seite 207-216, , Seite 207-216. https://doi.org/10.1109/ICDMW60847.2023.00033
Springer - Basic (author-date)Mirabi M, Nikiel RK, Binnig C (2023) Safe ML: a privacy-preserving Byzantine-robust framework for distributed machine learning training. 23 rd IEEE International Conference on Data Mining Workshops , Seite 207-216. https://doi.org/10.1109/ICDMW60847.2023.00033
Juristische Zitierweise (Stüber) (Deutsch)Mirabi, Meghdad/ Nikiel, René Klaus/ Binnig, Carsten, Safe ML: a privacy-preserving Byzantine-robust framework for distributed machine learning training, 23 rd IEEE International Conference on Data Mining Workshops 2023, , Seite 207-216.