Differential Privacy Trade-offs in Federated Malware Detection for Heterogeneous IoT Networks

Abstract

Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the integration of differential privacy in federated learning-based malware detection models affect the trade-off between model accuracy and communication efficiency, measured by F1-score. 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. 6 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the integration of differential privacy in federated learning-based malware detection models affect the trade-off between model accuracy and communication efficiency, measured by F1-score and bandwidth usage across heterogeneous IoT device networks?

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