Zobrazeno 1 - 10
of 263
pro vyhledávání: '"ZHANG Zikai"'
Publikováno v:
Gong-kuang zidonghua, Vol 50, Iss 8, Pp 52-60 (2024)
In response to the deformation and failure of surrounding rock in large section chamber underground roadways of coal mines, this study focuses on the sorting and filling of large section chamber underground roadways in Ulan Mulun coal mine. Similar s
Externí odkaz:
https://doaj.org/article/493211c853e84d33b52726397dd471f8
Backdoor attacks present a significant threat to the robustness of Federated Learning (FL) due to their stealth and effectiveness. They maintain both the main task of the FL system and the backdoor task simultaneously, causing malicious models to app
Externí odkaz:
http://arxiv.org/abs/2411.01040
Autor:
Tordeux, Antoine, Julitz, Tim M., Müller, Isabelle, Zhang, Zikai, Pietruschka, Jannis, Fricke, Nicola, Schlüter, Nadine, Bracke, Stefan, Löwer, Manuel
In the era of Industry 4.0, system reliability engineering faces both challenges and opportunities. On the one hand, the complexity of cyber-physical systems, the integration of novel numerical technologies, and the handling of large amounts of data
Externí odkaz:
http://arxiv.org/abs/2411.08913
Foundation models (FMs) have shown remarkable advancements in enhancing the performance of intelligent applications. To address the need for data privacy in FM fine-tuning, federated learning has emerged as the de facto framework. Specifically, Feder
Externí odkaz:
http://arxiv.org/abs/2410.10200
The Smart Grid (SG) is a critical energy infrastructure that collects real-time electricity usage data to forecast future energy demands using information and communication technologies (ICT). Due to growing concerns about data security and privacy i
Externí odkaz:
http://arxiv.org/abs/2409.10764
Federated Learning (FL) enables multiple clients to collaboratively train a model without sharing their local data. Yet the FL system is vulnerable to well-designed Byzantine attacks, which aim to disrupt the model training process by uploading malic
Externí odkaz:
http://arxiv.org/abs/2409.01435
Autor:
Zhang, Zikai, Hu, Rui
Federated learning (FL) is designed to preserve data privacy during model training, where the data remains on the client side (i.e., IoT devices), and only model updates of clients are shared iteratively for collaborative learning. However, this proc
Externí odkaz:
http://arxiv.org/abs/2309.03437
Publikováno v:
In Journal of Manufacturing Systems December 2024 77:1009-1026
Publikováno v:
In Applied Soft Computing November 2024 166
Publikováno v:
In Expert Systems With Applications 5 March 2025 263