Scaling Communication Overhead in Federated Learning for IoT Malware Detection

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the communication overhead in federated learning scale with the number of participating IoT devices when deploying real-time malware detection models on edge infrastructure. With the rapid development of the Internet of Everything (IoE), the number of smart devices connected to the Internet is increasing, resulting in large-scale data, which has caused problems such as bandwidth load, slow response speed, poor security, and poor privacy in. 12 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the communication overhead in federated learning scale with the number of participating IoT devices when deploying real-time malware detection models on edge infrastructure?

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