Lightweight Trustworthy Distributed Clustering
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912325965971456 |
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| author | Li, Hongyang Wu, Caesar Chadli, Mohammed Mammar, Said Bouvry, Pascal |
| author_facet | Li, Hongyang Wu, Caesar Chadli, Mohammed Mammar, Said Bouvry, Pascal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10109 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lightweight Trustworthy Distributed Clustering Li, Hongyang Wu, Caesar Chadli, Mohammed Mammar, Said Bouvry, Pascal Distributed, Parallel, and Cluster Computing Artificial Intelligence 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. |
| title | Lightweight Trustworthy Distributed Clustering |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2504.10109 |