Anika Hannemann

11 papers C 1Journal 6Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Understanding the Resource Cost of Fully Homomorphic Encryption in Quantum Federated Learning. CoRR Lukas Böhm, Arjhun Swaminathan, Anika Hannemann, Erik Buchmann
2024	—	`conf`	Differentially Private Multi-Label Learning Is Harder Than You'd Think. EuroS&P Workshops Anika Hannemann, Benjamin Friedl, Erik Buchmann
2024	—	`conf`	Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs. ICCS (4) Anika Hannemann, Jan Ewald, Leo Seeger, Erik Buchmann
2024	J	`jnl`	Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs. CoRR Anika Hannemann, Jan Ewald, Leo Seeger, Erik Buchmann
2024	J	`jnl`	PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies. CoRR Arjhun Swaminathan, Anika Hannemann, Ali Burak Ünal, Nico Pfeifer, Mete Akgün
2024	—	`conf`	Private, Efficient and Scalable Kernel Learning for Medical Image Analysis. CIBB Anika Hannemann, Arjhun Swaminathan, Ali Burak Ünal, Mete Akgün
2024	J	`jnl`	Private, Efficient and Scalable Kernel Learning for Medical Image Analysis. CoRR Anika Hannemann, Arjhun Swaminathan, Ali Burak Ünal, Mete Akgün
2023	J	`jnl`	A Privacy-Preserving Federated Learning Approach for Kernel methods. CoRR Anika Hannemann, Ali Burak Ünal, Arjhun Swaminathan, Erik Buchmann, Mete Akgün
2023	—	`conf`	A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods. TPS-ISA Anika Hannemann, Ali Burak Ünal, Arjhun Swaminathan, Erik Buchmann, Mete Akgün
2023	C	`conf`	Is Homomorphic Encryption Feasible for Smart Mobility? FedCSIS Anika Hannemann, Erik Buchmann
2023	J	`jnl`	Is Homomorphic Encryption Feasible for Smart Mobility? CoRR Anika Hannemann, Erik Buchmann