Federated Aggregation Frequency Effects on Lightweight Neural Network Convergence and Accuracy

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does reducing federated aggregation frequency impact the convergence rate and final accuracy of lightweight neural networks for anomaly detection on edge devices. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does reducing federated aggregation frequency impact the convergence rate and final accuracy of lightweight neural networks for anomaly detection 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