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20232026
most citedFederated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly

15 citations · 44 across the 17 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.LG2024

MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees

Ryan Zhang, Herbert Woisetschläger, Shiqiang Wang +1

Open-weight large language model (LLM) zoos allow users to quickly integrate state-of-the-art models into systems. Despite increasing availability, selecting the most appropriate m…

cs.LG2024

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…

cs.AI2024★ 1 cited

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…

cs.DC2024

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,…

cs.LG2024

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…

cs.LG2024★ 9 cited

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),…

cs.AI2024

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…

cs.LG2024★ 1 cited

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…