15 citations · 34 across the 4 of their papers we have counts for
Showing 2023Show all
2 papers · 1 filter
cs.LG2023★ 15 cited
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…
cs.LG2023★ 10 cited
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…