SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication.

ABSTRACT: Machine-to-machine (M2M) communication within the Internet of Things (IoT) faces increasing security and efficiency challenges as networks proliferate. Existing approaches often struggle with balancing robust security measures and energy efficiency, leading to vulnerabilities and reduced performance in resource-constrained environments. To address these limitations, we propose SAFE-CAST, a novel secure AI-federated enumeration for clustering-based automated surveillance and trust framework. This study addresses critical security and efficiency challenges in M2M communication within the context of IoT. SAFE-CAST integrates several innovative components: (1) a federated learning approach using Lloyd's K-means algorithm for secure clustering, (2) a quality diversity optimization algorithm (QD

SUBMITTER: Tuncel YK 

PROVIDER: S-EPMC11784817 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature