69 citations · 394 across the 33 of their papers we have counts for
9 papers · 1 filter
An End-to-End Performance Comparison of Seven Permissioned Blockchain Systems
Frank Christian Geyer, Hans-Arno Jacobsen, Ruben Mayer +1
The emergence of more and more blockchain solutions with innovative approaches to optimising performance, scalability, privacy and governance complicates performance analysis. Reas…
FabricCRDT: A Conflict-Free Replicated Datatypes Approach to Permissioned Blockchains
Pezhman Nasirifard, Ruben Mayer, Hans-Arno Jacobsen
With the increased adaption of blockchain technologies, permissioned blockchains such as Hyperledger Fabric provide a robust ecosystem for developing production-grade decentralized…
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly
Herbert Woisetschläger, Alexander Isenko, Shiqiang Wang +2
Large Language Models (LLM) and foundation models are popular as they offer new opportunities for individuals and businesses to improve natural language processing, interact with d…
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…
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…
How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study
Alexander Erben, Ruben Mayer, Hans-Arno Jacobsen
This paper aims to answer the question: Can deep learning models be cost-efficiently trained on a global market of spot VMs spanning different data centers and cloud providers? To…