提出轻量分布式聚类算法，保障边缘节点数据可信性。
Lightweight Trustworthy Distributed Clustering
- 用加性秘密共享实现分布式平均，更新聚类中心
- 无需中心节点，各节点间协作保持数据准确
- 适合资源受限的边缘计算场景，如智能城市
在资源受限的边缘计算系统（如自动驾驶传感器网络、工业物联网和智慧城市）中，如何在保证单个边缘节点数据可信性的前提下实现协同数据处理，是一个关键挑战。本文提出一种轻量级、完全分布式的k-means聚类算法，专为边缘环境设计。该方法在聚类中心更新阶段引入基于加性秘密共享的分布式平均机制，这是一种安全多方计算技术，确保各节点间协作时数据的准确性和可信性，无需依赖中心化协调，显著降低通信与计算开销。
原文摘要 · Abstract (English)
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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