Zobrazeno 1 - 10
of 879
pro vyhledávání: '"Werner, Stefan"'
Distributed sensors in the internet-of-things (IoT) generate vast amounts of sparse data. Analyzing this high-dimensional data and identifying relevant predictors pose substantial challenges, especially when data is preferred to remain on the device
Externí odkaz:
http://arxiv.org/abs/2408.05640
In the rapidly evolving internet-of-things (IoT) ecosystem, effective data analysis techniques are crucial for handling distributed data generated by sensors. Addressing the limitations of existing methods, such as the sub-gradient approach, which fa
Externí odkaz:
http://arxiv.org/abs/2408.01307
Autor:
Jäckel, Ulf, Neu, Silke, Albrecht, Christoph, Dreher, Elisabeth, Schirrmacher, Mike, Swaton, Thomas, Ullrich, Falk, Kober, Catrina, Göpfert, Katja, Rapp, Charlotte, Möller, Jörn, Peschke, Silke, Kurzer, Joachim, Köhler, Gabriele, Pölitz, Birgit, Schmidt, Albert, Nitsche, Doreen, Riehl, Gerhard, Füllner, Gerd, Jarosch, Ute, Rank, Harald, Laber, Hermann, Hager, Kerstin, Wuttke, Steffen, Klingner, Reinhardt, Kugler, Martina, Stamm, Sandra, Polzin, Jan, Werner, Stefan, Karalus, Wolfgang, Grünbeck-Bräuer, Anka
Der Ökolandbau in Sachsen entwickelt sich weiter und entwächst der Nische. Weiterhin deutlich sind jedoch traditionelle Schwächen, wie z. B. geringer Viehbesatz oder Marktferne, die die Entwicklung bremsen. Künftige Herausforderungen sind die Anp
Externí odkaz:
https://slub.qucosa.de/id/qucosa%3A72095
https://slub.qucosa.de/api/qucosa%3A72095/attachment/ATT-0/
https://slub.qucosa.de/api/qucosa%3A72095/attachment/ATT-0/
Nonnegative matrix factorization (NMF) is an effective data representation tool with numerous applications in signal processing and machine learning. However, deploying NMF in a decentralized manner over ad-hoc networks introduces privacy concerns du
Externí odkaz:
http://arxiv.org/abs/2403.18326
We introduce a distributed algorithm, termed noise-robust distributed maximum consensus (RD-MC), for estimating the maximum value within a multi-agent network in the presence of noisy communication links. Our approach entails redefining the maximum c
Externí odkaz:
http://arxiv.org/abs/2403.18509
Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicious clients tamper with their local models to manipulate the global mod
Externí odkaz:
http://arxiv.org/abs/2403.13108
This paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD). The non-smooth and non-convex nature of these problem
Externí odkaz:
http://arxiv.org/abs/2309.03094
This paper proposes a proximal variant of the alternating direction method of multipliers (ADMM) for distributed optimization. Although the current versions of ADMM algorithm provide promising numerical results in producing solutions that are close t
Externí odkaz:
http://arxiv.org/abs/2308.16752
This paper addresses the problem of localization, which is inherently non-convex and non-smooth in a federated setting where the data is distributed across a multitude of devices. Due to the decentralized nature of federated environments, distributed
Externí odkaz:
http://arxiv.org/abs/2308.16737
This paper proposes a Byzantine-resilient consensus-based distributed filter (BR-CDF) wherein network agents employ partial sharing of state parameters. We characterize the performance and convergence of the BR-CDF and study the impact of a coordinat
Externí odkaz:
http://arxiv.org/abs/2307.13906