| Federated machine learning, privacy-enhancing technologies, and data protection laws in medical research: scoping review A Brauneck, L Schmalhorst, MM Kazemi Majdabadi, M Bakhtiari, U Völker, ... Journal of Medical Internet Research 25, e41588, 2023 | 46 | 2023 |
| Federated machine learning in data-protection-compliant research A Brauneck, L Schmalhorst, MM Kazemi Majdabadi, M Bakhtiari, U Völker, ... Nature Machine Intelligence 5 (1), 2-4, 2023 | 21 | 2023 |
| Flimma: a federated and privacy-aware tool for differential gene expression analysis O Zolotareva, R Nasirigerdeh, J Matschinske, R Torkzadehmahani, ... Genome biology 22 (1), 338, 2021 | 20 | 2021 |
| The FeatureCloud platform for federated learning in biomedicine: unified approach J Matschinske, J Späth, M Bakhtiari, N Probul, MM Kazemi Majdabadi, ... Journal of Medical Internet Research 25, e42621, 2023 | 15 | 2023 |
| Privacy-aware multi-institutional time-to-event studies J Späth, J Matschinske, FK Kamanu, SA Murphy, O Zolotareva, ... PLOS Digital Health 1 (9), e0000101, 2022 | 10 | 2022 |
| Federated multi-mini-batch: an efficient training approach to federated learning in Non-IID environments R Nasirigerdeh, M Bakhtiari, R Torkzadehmahani, A Bayat, M List, ... arXiv preprint arXiv:2011.07006, 2020 | 9 | 2020 |
| The featurecloud ai store for federated learning in biomedicine and beyond J Matschinske, J Späth, R Nasirigerdeh, R Torkzadehmahani, ... arXiv preprint arXiv:2105.05734, 2021 | 8 | 2021 |
Jan BaumbachUniversity of Hamburg在 uni-hamburg.de 的电子邮件经过验证
