Non-IID Data Distribution Effects on Federated Malware Detection Performance

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does varying the degree of non-IID data distribution across IoT nodes impact the convergence rate and final F1-score of federated malware detection models compared to centralized baselines. In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does varying the degree of non-IID data distribution across IoT nodes impact the convergence rate and final F1-score of federated malware detection models compared to centralized baselines?

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