Federated Learning Trade-offs in IoT Security: Communication Overhead vs. Adversarial Robustness

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the trade-off between communication overhead and model robustness against adversarial attacks in federated learning systems for IoT security. Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its. 0 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

What is the trade-off between communication overhead and model robustness against adversarial attacks in federated learning systems for IoT security?

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