1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.IR2024
Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports
Malte Tölle, Lukas Burger, Halvar Kelm +21
Purpose: Federated training is often hindered by heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and dispar…
cs.CV2024
FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels
Malte Tölle, Fernando Navarro, Sebastian Eble +3
Federated learning is one popular paradigm to train a joint model in a distributed, privacy-preserving environment. But partial annotations pose an obstacle meaning that categories…
cs.CR2022★ 1 cited
Content-Aware Differential Privacy with Conditional Invertible Neural Networks
Malte Tölle, Ullrich Köthe, Florian André +2
Differential privacy (DP) has arisen as the gold standard in protecting an individual's privacy in datasets by adding calibrated noise to each data sample. While the application to…