Recommending plant taxa for supporting on-site species identification. - Hans Christian Wittich; Marco Seeland; Jana Wäldchen; Michael Rzanny; Patrick Mäder (BMC Bioinform., 2018)

Automated plant species identification - Trends and future directions. - Jana Wäldchen; Michael Rzanny; Marco Seeland; Patrick Mäder (PLoS Comput. Biol., 2018)

Image-based classification of plant genus and family for trained and untrained plant species. - Marco Seeland; Michael Rzanny; David Boho; Jana Wäldchen; Patrick Mäder (BMC Bioinform., 2019)

Efficiently Annotating Object Images with Absolute Size Information Using Mobile Devices. - Martin Hofmann 019; Marco Seeland; Patrick Mäder (Int. J. Comput. Vis., 2019)

Flora Capture: a citizen science application for collecting structured plant observations. - David Boho; Michael Rzanny; Jana Wäldchen; Fabian Nitsche; Alice Deggelmann; Hans Christian Wittich; Marco Seeland; Patrick Mäder (BMC Bioinform., 2020)

PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage. - Daniel Scheliga; Patrick Mäder; Marco Seeland (WACV, 2022)

Combining Variational Modeling with Partial Gradient Perturbation to Prevent Deep Gradient Leakage. - Daniel Scheliga; Patrick Mäder; Marco Seeland (CoRR, 2022)

Dropout Is NOT All You Need to Prevent Gradient Leakage. - Daniel Scheliga; Patrick Maeder; Marco Seeland (AAAI, 2023)

Collaboration Management for Federated Learning. - Marius Schlegel; Daniel Scheliga; Kai-Uwe Sattler; Marco Seeland; Patrick Mäder (ICDEW, 2024)

Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data. - Daniel Scheliga; Patrick Mäder; Marco Seeland (Appl. Artif. Intell., 2024)

Privacy preserving federated learning with convolutional variational bottlenecks. - Daniel Scheliga; Patrick Mäder; Marco Seeland (Cybersecur., 2025)