Marco Seeland

According to our database, Marco Seeland authored at least 16 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Lowering barriers to federated learning: collaboration management and provenance.

Marius Schlegel, Daniel Scheliga, Kai-Uwe Sattler, Marco Seeland, Patrick Mäder

J. Big Data, December, 2026

Efficient Brood Cell Detection in Layer Trap Nests for Bees and Wasps: Balancing Labeling Effort and Species Coverage.

Chenchang Liu, Felix Fornoff, Annika Grasreiner, Patrick Maeder, Henri Greil, Marco Seeland

CoRR, March, 2026

Model utility and explainability in federated learning - A case study in healthcare using fundus oculi datasets.

Niklas Penzel, Daniel Scheliga, Hannes Oppermann, Patrick Mäder, Jens Haueisen, Joachim Denzler, Marco Seeland

J. Biomed. Informatics, 2026

Deep learning based detection of wild bee parasites under natural conditions.

Svetlana Ionova, Henri Greil, Patrick Mäder, Marco Seeland

Ecol. Informatics, 2026

Order-Level Arthropod Detection Using Deep Learning: Addressing Scale Variability through Synthetic Data.

Chenchang Liu, Svetlana Ionova, Patrick Mäder, Marco Seeland

Proceedings of the 21st International Conference on Computer Vision Theory and Applications, 2026

2025

Privacy preserving federated learning with convolutional variational bottlenecks.

Daniel Scheliga, Patrick Mäder, Marco Seeland

Cybersecur., December, 2025

2024

Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data.

Daniel Scheliga, Patrick Mäder, Marco Seeland

Appl. Artif. Intell., December, 2024

Collaboration Management for Federated Learning.

Marius Schlegel, Daniel Scheliga, Kai-Uwe Sattler, Marco Seeland, Patrick Mäder

Proceedings of the 40th International Conference on Data Engineering, ICDE 2024, 2024

2023

Dropout Is NOT All You Need to Prevent Gradient Leakage.

Daniel Scheliga, Patrick Maeder, Marco Seeland

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Combining Variational Modeling with Partial Gradient Perturbation to Prevent Deep Gradient Leakage.

Daniel Scheliga, Patrick Mäder, Marco Seeland

CoRR, 2022

PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage.

Daniel Scheliga, Patrick Mäder, Marco Seeland

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

2020

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

2019

Efficiently Annotating Object Images with Absolute Size Information Using Mobile Devices.

Martin Hofmann, Marco Seeland, Patrick Mäder

Int. J. Comput. Vis., 2019

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

2018

Automated plant species identification - Trends and future directions.

Jana Wäldchen, Michael Rzanny, Marco Seeland, Patrick Mäder

PLoS Comput. Biol., 2018

Recommending plant taxa for supporting on-site species identification.

Hans Christian Wittich, Marco Seeland, Jana Wäldchen, Michael Rzanny, Patrick Mäder

BMC Bioinform., 2018