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HARTMANN Lena Maria

University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG

ORCID
0000-0002-2179-3703
Main Referenced Co-authors
BOUVRY, Pascal  (7)
DANOY, Grégoire  (7)
ALSWAITTI, Mohammed  (3)
Main Referenced Keywords
Federated Learning (4); Federated Multi-objective Learning (3); and Cluster Computing (2); Computer Science - Distributed (2); Computer Science - Learning (2);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > PCOG - Parallel Computing & Optimization Group (3)
ULHPC - University of Luxembourg: High Performance Computing (2)
Main Referenced Disciplines
Computer science (7)
Engineering, computing & technology: Multidisciplinary, general & others (1)

Publications (total 8)

The most downloaded
204 downloads
HARTMANN, L. M., DANOY, G., ALSWAITTI, M., & BOUVRY, P. (2023). JoVe-FL - A Joint-embedding Vertical Federated Learning Framework [Paper presentation]. International Conference on Agents and Artificial Intelligence. doi:10.5220/0011802600003393 https://hdl.handle.net/10993/55869

The most cited

2 citations (Scopus®)

HARTMANN, L. M., DANOY, G., & BOUVRY, P. (2025). FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences. ACM Transactions on Modeling and Performance Evaluation of Computing Systems. doi:10.1145/3708984 https://hdl.handle.net/10993/63247

HARTMANN, L. M. (2026). Multi-objective Learning in Federation [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/67717

HARTMANN, L. M., DANOY, G., & BOUVRY, P. (18 August 2025). Multi-objective methods in Federated Learning: A survey and taxonomy [Paper presentation]. International Workshop on Federated Learning with Generative AI In Conjunction with IJCAI 2025 (FedGenAI-IJCAI'25), Montréal, Canada. doi:10.48550/arXiv.2502.03108
Peer reviewed

HARTMANN, L. M., DANOY, G., & BOUVRY, P. (2025). FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences. ACM Transactions on Modeling and Performance Evaluation of Computing Systems. doi:10.1145/3708984
Peer reviewed

HARTMANN, L. M., DANOY, G., & BOUVRY, P. (17 September 2024). Heterogeneity: An Open Challenge for Federated On-board Machine Learning [Paper presentation]. SPAICE conference, United Kingdom.
Peer reviewed

HARTMANN, L. M., DANOY, G., & BOUVRY, P. (2024). Introducing FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences [Paper presentation]. Multi-Objective Decision Making Workshop at ECAI 2024, Santiago de Compostela, Spain.
Peer reviewed

HARTMANN, L. M., DANOY, G., ALSWAITTI, M., & BOUVRY, P. (December 2023). MOFL/D: A Federated Multi-objective Learning Framework with Decomposition [Paper presentation]. International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023 (FL@FM-NeurIPS’23), New Orleans, United States - Louisiana.
Peer reviewed

HARTMANN, L. M., DANOY, G., ALSWAITTI, M., & BOUVRY, P. (2023). JoVe-FL - A Joint-embedding Vertical Federated Learning Framework [Paper presentation]. International Conference on Agents and Artificial Intelligence. doi:10.5220/0011802600003393

HARTMANN, L. M., DANOY, G., ALSWAITTI, M., & BOUVRY, P. (2023). A split-training approach to JoVe-FL [Paper presentation]. International Conference on Optimization and Learning.
Peer reviewed

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