Quantum-Enhanced Federated Learning for Malware Detection in Heterogeneous IoT Networks

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the integration of quantum-enhanced federated learning affect the accuracy and convergence rate of malware detection models compared to classical federated learning in heterogeneous IoT. This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this context, a framework that uses federated learning to detect malware affecting IoT devices is. 12 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.4/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the integration of quantum-enhanced federated learning affect the accuracy and convergence rate of malware detection models compared to classical federated learning in heterogeneous IoT networks, measured by F1-score and round convergence time?

Verification Level

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

Truth-Engine Gate Verdict

This record has not completed Gate 2 of the verification pipeline (a type-checked Lean4 proof for mathematical claims, or a sealed-sandbox reproduction for empirical claims). It is a literature synthesis only. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

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