Lightweight Trustworthy Distributed Clustering

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.

@article{li2025_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 }
}