Non-IID Data Distribution Effects on Federated Intrusion Detection Convergence and Accuracy

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: What is the impact of non-IID data distribution on the convergence speed and detection accuracy of federated intrusion detection systems under label-flipping attacks. Cyber intrusion attacks that compromise the users' critical and sensitive data are escalating in volume and intensity, especially with the growing connections between our daily life and the Internet. The large volume and high complexity of such intrusion attacks have impeded the. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 1.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of non-IID data distribution on the convergence speed and detection accuracy of federated intrusion detection systems under label-flipping attacks?

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