FedQuad Performance in Non-IID Intrusion Detection Benchmarks Against FedAvg and FedProx

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does FedQuad's performance compare to existing federated learning methods (e.g., FedAvg, FedProx) in terms of F1-score and false positive rate when evaluated on standardized intrusion detection. Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting training of deep anomaly detection models with additional labeled anomaly samples. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does FedQuad's performance compare to existing federated learning methods (e.g., FedAvg, FedProx) in terms of F1-score and false positive rate when evaluated on standardized intrusion detection benchmarks like NSL-KDD or CICIDS2017 under non-IID settings?

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