Lightweight Trustworthy Distributed Clustering

2025-04-14 · Hongyang Li, Caesar Wu, Mohammed Chadli, Said Mammar, Pascal Bouvry

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.

📄 PDF Abstract BibTeX arXiv:2504.10109

Code (0)

등록된 구현이 없습니다.

Tasks

ClusteringEdge-computing

Methods 이 논문이 사용한 방법론

k-Means Clustering k-Means Clustering is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$…

Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance

2024-02-02 · Wenqi Wei, Ling Liu

Emerging Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact. However, recent studies have identified new attack surfaces and risks cau…

Fairness

PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT

2026-02-20 · Phan The Duy, Nghi Hoang Khoa, Nguyen Tran Anh Quan, Luong Ha Tien 외 arxiv

This paper proposes PenTiDef, a fully decentralized, privacy-preserving, and poisoning-resilient framework for decentralized federated IDS (DFL-IDS). PenTiDef synergistically integrates three key components: (i) client-s…

Intrusion Detection

Discovering Collaboration from Novelty: Random Network Distillation for Clustered Federated Learning

2026-06-29 · Davide Domini, Gianluca Aguzzi, Ivana Dusparic, Danilo Pianini 외 arxiv

Federated Learning often suffers under non-independently and identically distributed data, where a single global model may fail to represent the diversity of client distributions. Clustered Federated Learning mitigates t…

Federated Learning

On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM)

2024-12-03 · Zhaohui Yang, Wei Xu, Le Liang, Yuanhao Cui 외

Combining wireless communication with large artificial intelligence (AI) models can open up a myriad of novel application scenarios. In sixth generation (6G) networks, ubiquitous communication and computing resources all…

Autonomous Vehicles Ethics

Distributed Clustering based on Distributional Kernel

2024-09-14 · Hang Zhang, Yang Xu, Lei Gong, Ye Zhu 외

This paper introduces a new framework for clustering in a distributed network called Distributed Clustering based on Distributional Kernel (K) or KDC that produces the final clusters based on the similarity with respect …

Clustering