Lightweight Trustworthy Distributed Clustering

• URL: http://arxiv.org/abs/2504.10109v1
• Date: Mon, 14 Apr 2025 11:16:07 GMT
• Title: Lightweight Trustworthy Distributed Clustering
• Authors: Hongyang Li, Caesar Wu, Mohammed Chadli, Said Mammar, Pascal Bouvry,
• Abstract summary: This paper presents a lightweight, fully distributed k-means clustering algorithm specifically adapted for edge environments.<n>It uses a distributed averaging approach with additive secret sharing, a secure multiparty technique, during the cluster center update phase to ensure the accuracy and trustworthiness of data across nodes.
• Score: 22.41687499847953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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