Federated Learning Convergence and Communication Efficiency with Adaptive Aggregation in IoT Malware Detection

Abstract

Abstract: This report synthesises findings from 5 peer-reviewed papers addressing the following research question: What is the effect of varying the number of participating IoT clients in a federated learning setup on the convergence rate and communication efficiency when using adaptive aggregation rules for. This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this context, a framework that uses federated learning to detect malware affecting IoT devices is. 10 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the effect of varying the number of participating IoT clients in a federated learning setup on the convergence rate and communication efficiency when using adaptive aggregation rules for malware detection?

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