Daniel Scheliga

12 papers A* 1A 1Journal 8Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Lowering barriers to federated learning: collaboration management and provenance. J. Big Data Marius Schlegel, Daniel Scheliga, Kai-Uwe Sattler, Marco Seeland, Patrick Mäder
2026	J	`jnl`	Model utility and explainability in federated learning - A case study in healthcare using fundus oculi datasets. J. Biomed. Informatics Niklas Penzel, Daniel Scheliga, Hannes Oppermann, Patrick Mäder, Jens Haueisen, Joachim Denzler, Marco Seeland
2025	—	`—`	Federated learning with privacy modules to resist gradient inversion attacks. Daniel Scheliga
2025	J	`jnl`	Privacy preserving federated learning with convolutional variational bottlenecks. Cybersecur. Daniel Scheliga, Patrick Mäder, Marco Seeland
2024	—	`conf`	Collaboration Management for Federated Learning. ICDEW Marius Schlegel, Daniel Scheliga, Kai-Uwe Sattler, Marco Seeland, Patrick Mäder
2024	J	`jnl`	Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data. Appl. Artif. Intell. Daniel Scheliga, Patrick Mäder, Marco Seeland
2023	A*	`conf`	Dropout Is NOT All You Need to Prevent Gradient Leakage. AAAI Daniel Scheliga, Patrick Maeder, Marco Seeland
2023	J	`jnl`	Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks. CoRR Daniel Scheliga, Patrick Mäder, Marco Seeland
2022	J	`jnl`	Combining Variational Modeling with Partial Gradient Perturbation to Prevent Deep Gradient Leakage. CoRR Daniel Scheliga, Patrick Mäder, Marco Seeland
2022	J	`jnl`	Dropout is NOT All You Need to Prevent Gradient Leakage. CoRR Daniel Scheliga, Patrick Mäder, Marco Seeland
2022	A	`conf`	PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage. WACV Daniel Scheliga, Patrick Mäder, Marco Seeland
2021	J	`jnl`	PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage. CoRR Daniel Scheliga, Patrick Mäder, Marco Seeland