Zobrazeno 1 - 10
of 7 409
pro vyhledávání: '"Assen, A."'
Autor:
Feng, Chao, Guan, Hongjie, Celdrán, Alberto Huertas, von der Assen, Jan, Bovet, Gérôme, Stiller, Burkhard
Federated Learning (FL) performance is highly influenced by data distribution across clients, and non-Independent and Identically Distributed (non-IID) leads to a slower convergence of the global model and a decrease in model effectiveness. The exist
Externí odkaz:
http://arxiv.org/abs/2410.07678
Autor:
Feng, Chao, Celdrán, Alberto Huertas, Zeng, Zien, Ye, Zi, von der Assen, Jan, Bovet, Gerome, Stiller, Burkhard
Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserved manner, is still vulnerable to poisoning attacks where malicious clients tamper with data or models. Current defense methods often assume Independently an
Externí odkaz:
http://arxiv.org/abs/2409.19302
Verifying the integrity of embedded device characteristics is required to ensure secure operation of a device. One central challenge is to securely extract and store device-specific configurations for future verification. Existing device attestation
Externí odkaz:
http://arxiv.org/abs/2407.14286
Autor:
Jürgens, Boyung, Seele, Hagen, Schricker, Hendrik, Reinert, Christiane, von der Assen, Niklas
Stochastic programming is widely used for energy system design optimization under uncertainty but can exponentially increase the computational complexity with the number of scenarios. Common scenario reduction techniques, like moments-matching or dis
Externí odkaz:
http://arxiv.org/abs/2407.11457
Autor:
Feng, Chao, Celdrán, Alberto Huertas, von der Assen, Jan, Beltrán, Enrique Tomás Martínez, Bovet, Gérôme, Stiller, Burkhard
Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a central s
Externí odkaz:
http://arxiv.org/abs/2407.08652
Threat modeling has been successfully applied to model technical threats within information systems. However, a lack of methods focusing on non-technical assets and their representation can be observed in theory and practice. Following the voices of
Externí odkaz:
http://arxiv.org/abs/2406.01135
The advent of Decentralized Physical Infrastructure Networks (DePIN) represents a shift in the digital infrastructure of today's Internet. While Centralized Service Providers (CSP) monopolize cloud computing, DePINs aim to enhance data sovereignty an
Externí odkaz:
http://arxiv.org/abs/2404.08306
Publikováno v:
Journal of Fluid Mechanics. 2024;995:A10.
We use three-dimensional direct numerical simulations of homogeneous isotropic turbulence in a cubic domain to investigate the dynamics of heavy, chiral, finite-size inertial particles and their effects on the flow. Using an immersed-boundary method
Externí odkaz:
http://arxiv.org/abs/2404.04217
Autor:
von der Assen, Jan, Sharif, Jamo, Feng, Chao, Killer, Christian, Bovet, Gérôme, Stiller, Burkhard
Threat modeling is a popular method to securely develop systems by achieving awareness of potential areas of future damage caused by adversaries. However, threat modeling for systems relying on Artificial Intelligence is still not well explored. Whil
Externí odkaz:
http://arxiv.org/abs/2403.06512
Threat modeling has emerged as a key process for understanding relevant threats within businesses. However, understanding the importance of threat events is rarely driven by the business incorporating the system. Furthermore, prioritization of threat
Externí odkaz:
http://arxiv.org/abs/2402.14140