Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines. - Alexander Isenko; Ruben Mayer; Jeffrey Jedele; Hans-Arno Jacobsen (SIGMOD Conference, 2022)

On Serving Image Classification Models. - Aurora González-Vidal; Alexander Isenko; K. R. Jayaram (WOSC@Middleware, 2023)

FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems. - Herbert Woisetschläger; Alexander Isenko; Ruben Mayer; Hans-Arno Jacobsen (CoRR, 2023)

Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly. - Herbert Woisetschläger; Alexander Erben; Shiqiang Wang 001; Ruben Mayer; Hans-Arno Jacobsen (DEEM@SIGMOD, 2024)

A Survey on Efficient Federated Learning Methods for Foundation Model Training. - Herbert Woisetschläger; Alexander Erben; Shiqiang Wang 001; Ruben Mayer; Hans-Arno Jacobsen (IJCAI, 2024)

FLEdge: Benchmarking Federated Learning Applications in Edge Computing Systems. - Herbert Woisetschläger; Alexander Erben; Ruben Mayer; Shiqiang Wang 001; Hans-Arno Jacobsen (Middleware, 2024)

Federated Learning Priorities Under the European Union Artificial Intelligence Act. - Herbert Woisetschläger; Alexander Erben; Bill Marino; Shiqiang Wang 001; Nicholas D. Lane; Ruben Mayer; Hans-Arno Jacobsen (CoRR, 2024)

Performance Optimization in Deep Learning Systems. - Alexander Erben (2024)

How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study. - Alexander Erben; Ruben Mayer; Hans-Arno Jacobsen (Proc. VLDB Endow., 2024)