Federated Learning Rounds Impact on Model Performance and Communication Efficiency in IoT Malware Detection

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: What is the impact of varying the number of federated learning rounds on the model performance (accuracy, F1-score) and communication efficiency (throughput, bandwidth usage) when training on N-BaIoT. In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning.. 11 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of varying the number of federated learning rounds on the model performance (accuracy, F1-score) and communication efficiency (throughput, bandwidth usage) when training on N-BaIoT for IoT malware detection?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Truth-Engine Gate Verdict

This record has not completed Gate 2 of the verification pipeline (a type-checked Lean4 proof for mathematical claims, or a sealed-sandbox reproduction for empirical claims). It is a literature synthesis only. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

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