# Lightweight Trustworthy Distributed Clustering

arXiv: https://arxiv.org/abs/2504.10109 · HTML: https://mlsanity.com/paper/2504.10109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Distributed Optimization Using the Primal-Dual Method of Multipliers](https://mlsanity.com/paper/1702.00841/) (2017)
- [Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus](https://mlsanity.com/paper/2312.07947/) (2023)
- [On the privacy of federated Clustering: A Cryptographic View](https://mlsanity.com/paper/2312.07992/) (2023)
- [Trustworthiness of Stochastic Gradient Descent in Distributed Learning](https://mlsanity.com/paper/2410.21491/) (2024)

## Built on by (most cited first, 1 of 1)

- [Trustworthy Efficient Communication for Distributed Learning using LQ-SGD Algorithm](https://mlsanity.com/paper/2506.17974/) (2025)

## Abstract (arXiv)

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.