Federated Learning Aggregation Rules and Their Impact on Edge-Based Intrusion Detection Efficiency

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How do different aggregation rules in federated learning affect the inference efficiency and detection latency of deep learning-based intrusion detection systems on edge devices. Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. The development of deep learning technologies opened the door to build more complex and effective threat detection models. 13 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do different aggregation rules in federated learning affect the inference efficiency and detection latency of deep learning-based intrusion detection systems on edge devices?

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