Collaborative Data Processing; Distributed Computing; Distributed Learning; Edge Computing; k-means Clustering; Trustworthiness; Collaborative data processing; Computing system; Critical challenges; Distributed clustering; Distributed learning; Edge computing; Edge nodes; K-means++ clustering; Sensors network; Computer Science Applications; Decision Sciences (miscellaneous); Information Systems and Management; Control and Optimization; Modeling and Simulation
Abstract :
[en] Ensuring data trustworthiness within individual edge nodes while facilitating collaborative data processing poses a critical challenge in edge computing systems (ECS), particularly in resource-constrained scenarios such as autonomous systems sensor networks, industrial IoT, and smart cities. This paper presents a lightweight, fully distributed k-means clustering algorithm specifically adapted for edge environments, leveraging a distributed averaging approach with additive secret sharing, a secure multiparty computation technique, during the cluster center update phase to ensure the accuracy and trustworthiness of data across nodes.
Disciplines :
Computer science
Author, co-author :
LI, Hongyang ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG
Wu, Caesar; University of Luxembourg, Luxembourg
Chadli, Mohammed; University Paris-Saclay, France
Mammar, Said; University Paris-Saclay, France
BOUVRY, Pascal ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
External co-authors :
yes
Language :
English
Title :
Lightweight Trustworthy Distributed Clustering
Publication date :
05 August 2025
Event name :
2025 37th Chinese Control and Decision Conference (CCDC)
Event place :
Xiamen, Chn
Event date :
16-05-2025 => 19-05-2025
Audience :
International
Main work title :
Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025
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