Scaling Federated Clients in Lightweight Malware Detection on Edge Devices

Abstract

Abstract: This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does increasing the number of federated clients impact the inference latency and throughput of lightweight malware detection models on edge devices when evaluated on the N-BaIoT dataset. 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; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does increasing the number of federated clients impact the inference latency and throughput of lightweight malware detection models on edge devices when evaluated on the N-BaIoT dataset?

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