Robustness and False Positives in Federated Intrusion Detection Under Heterogeneous Edge Compute

Abstract

Abstract: This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does heterogeneity in edge device compute capabilities affect the robustness and false positive rates of federated deep learning intrusion detection systems using non-IID data distributions. Federated Learning (FL) provides decentralised model training, which effectively tackles problems such as distributed data and privacy preservation. However, the generalisation of global models frequently faces challenges from data heterogeneity among clients. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does heterogeneity in edge device compute capabilities affect the robustness and false positive rates of federated deep learning intrusion detection systems using non-IID data distributions?

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