69 citations · 394 across the 33 of their papers we have counts for
37 papers
Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study
Nikolai Merkel, Pierre Toussing, Ruben Mayer +1
Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative…
Federated Learning and AI Regulation in the European Union: Who is Responsible? -- An Interdisciplinary Analysis
Herbert Woisetschläger, Simon Mertel, Christoph Krönke +2
The European Union Artificial Intelligence Act mandates clear stakeholder responsibilities in developing and deploying machine learning applications to avoid substantial fines, pri…
Should my Blockchain Learn to Drive? A Study of Hyperledger Fabric
Jeeta Ann Chacko, Ruben Mayer, Hans-Arno Jacobsen
Similar to other transaction processing frameworks, blockchain systems need to be dynamically reconfigured to adapt to varying workloads and changes in network conditions. However,…
Federated Computing -- Survey on Building Blocks, Extensions and Systems
René Schwermer, Ruben Mayer, Hans-Arno Jacobsen
In response to the increasing volume and sensitivity of data, traditional centralized computing models face challenges, such as data security breaches and regulatory hurdles. Feder…
Federated Learning Priorities Under the European Union Artificial Intelligence Act
Herbert Woisetschläger, Alexander Erben, Bill Marino +4
The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way. Our key inquiry is how this will affect Federated Learning (FL),…
Choosing a Classical Planner with Graph Neural Networks
Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen +2
Online planner selection is the task of choosing a solver out of a predefined set for a given planning problem. As planning is computationally hard, the performance of solvers vari…