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Lightweight Trustworthy Distributed Clustering
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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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Cited by 1 Pith paper
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Trustworthy Efficient Communication for Distributed Learning using LQ-SGD Algorithm
LQ-SGD combines low-rank gradient compression with log quantization, claiming roughly 4x lower communication than PowerSGD while preserving accuracy and improving gradient-inversion resistance.
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