no code implementations • 3 Apr 2024 • René Schwermer, Ruben Mayer, Hans-Arno Jacobsen
FC comprises of Federated Learning (FL) and Federated Analytics (FA).
no code implementations • 5 Feb 2024 • Herbert Woisetschläger, Alexander Erben, Bill Marino, Shiqiang Wang, Nicholas D. Lane, Ruben Mayer, Hans-Arno Jacobsen
The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way.
no code implementations • 25 Jan 2024 • Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen, Horst Samulowitz, Michael Katz
Thus, the ability to predict their performance on a given problem is of great importance.
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 • 29 Aug 2023 • Nikolai Merkel, Daniel Stoll, Ruben Mayer, Hans-Arno Jacobsen
In this paper, we study the effectiveness of graph partitioning for distributed GNN training.
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 • 5 Jun 2023 • 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?
no code implementations • 23 May 2023 • Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen
Graph Neural Networks (GNNs) are an emerging research field.
no code implementations • 26 Aug 2022 • Matthias Kahl, Daniel Jorde, Hans-Arno Jacobsen
On the basis of an event-based appliance recognition approach, we evaluate seven different classification models: a classical machine learning approach that is based on a hand-crafted feature extraction, three different deep neural network architectures for automated feature extraction on raw waveform data, as well as three baseline approaches for raw data processing.
1 code implementation • 23 Jun 2022 • Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer, Hans-Arno Jacobsen
The DEBS Grand Challenge (GC) is an annual programming competition open to practitioners from both academia and industry.
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.
no code implementations • 27 Mar 2019 • Ruben Mayer, Hans-Arno Jacobsen
One of the reasons for this success is the increasing size of DL models and the proliferation of vast amounts of training data being available.
no code implementations • 1 Mar 2019 • Amirhesam Shahvarani, Hans-Arno Jacobsen
There is increasing interest in using multicore processors to accelerate stream processing.
Databases
no code implementations • 13 Nov 2018 • Fei Pan, Hans-Arno Jacobsen
In this paper, we present a new algorithm called PanJoin which has high throughput on large windows and supports both equi-join and non-equi-join.
Databases
no code implementations • 12 Aug 2017 • Christoph Doblander, Martin Strohbach, Holger Ziekow, Hans-Arno Jacobsen
This paper addresses the use of smart-home sensor streams for continuous prediction of energy loads of individual households which participate as an agent in local markets.
no code implementations • 1 Apr 2014 • Andreas Veit, Christoph Goebel, Rohit Tidke, Christoph Doblander, Hans-Arno Jacobsen
The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible loads and energy storage.