8 papers
FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
Herbert Woisetschläger, Alexander Erben, Ruben Mayer +2
Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. Heterogeneous hardware, unreliable client devic…
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…
A Survey on Efficient Federated Learning Methods for Foundation Model Training
Herbert Woisetschläger, Alexander Isenko, Shiqiang Wang +2
Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. However, new approaches to FL oft…
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
Nikolai Merkel, Daniel Stoll, Ruben Mayer +1
Recently, graph neural networks (GNNs) have gained much attention as a growing area of deep learning capable of learning on graph-structured data. However, the computational and me…
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,…