Lightweight Trustworthy Distributed Clustering
Distributed, Parallel, and Cluster Computing
2025-04-15 v1 Artificial Intelligence
Abstract
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.
Cite
@article{arxiv.2504.10109,
title = {Lightweight Trustworthy Distributed Clustering},
author = {Hongyang Li and Caesar Wu and Mohammed Chadli and Said Mammar and Pascal Bouvry},
journal= {arXiv preprint arXiv:2504.10109},
year = {2025}
}