Quantization Impact on Throughput in Adversarial and Non-Adversarial Multilingual Rumor Detection at the Edge

Abstract

Abstract: This report synthesises findings from 5 peer-reviewed papers addressing the following research question: What is the impact of model quantization on the inference throughput of adversarial versus non-adversarial contrastive pre-trained multilingual rumor detection models when deployed on edge devices. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of model quantization on the inference throughput of adversarial versus non-adversarial contrastive pre-trained multilingual rumor detection models when deployed on edge devices like NVIDIA Jetson?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance