Lightweight Trustworthy Distributed Clustering

Ensuring data trustworthiness within individual edge node s while facilitating collaborative data processing poses a critical challenge in edge computing system s (ECS), particularly in resource-constrained scenarios such as autonomous systems sensor networks, indu strial IoT, and smart cities. This paper presents a lightweight, fully distributed k -means clustering algorithm specifically adapted for edge e nvi-ronments, leveraging a distributed averaging approach wit h additive secret sharing, a secure multiparty computation technique, during the cluster center update ph ase to ensure the accuracy and trustworthiness of data across nodes. Edge computing, a paradigm emerging from distributed compu ting, emphasizes processing data at or near its source to minimize latency and reduce band width consumption [1]-[3]. The rapid advancements in edge computing technologies, includ ing algorithms for decentralized and efficient data processing, have significantly accelerated t he deployment of distributed sensor networks. Two key properties of ECS are crucial in large-scale deploym ents.