no code implementations • 9 Jan 2024 • Herbert Woisetschläger, Alexander Isenko, Shiqiang Wang, Ruben Mayer, Hans-Arno Jacobsen
We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications, elaborate on the readiness of FL frameworks to work with FMs and provide future research opportunities on how to evaluate generative models in FL as well as the interplay of privacy and PEFT.
no code implementations • 4 Oct 2023 • Herbert Woisetschläger, Alexander Isenko, Shiqiang Wang, Ruben Mayer, Hans-Arno Jacobsen
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 data, and retrieve information faster.
no code implementations • 8 Jun 2023 • Herbert Woisetschläger, Alexander Isenko, Ruben Mayer, Hans-Arno Jacobsen
Federated Machine Learning (FL) has received considerable attention in recent years.
1 code implementation • 17 Feb 2022 • Alexander Isenko, Ruben Mayer, Jeffrey Jedele, Hans-Arno Jacobsen
As a consequence of this development, data preprocessing and provisioning are becoming a severe bottleneck in end-to-end deep learning pipelines.