Federated Learning Aggregation Strategies for IoT Networks: Communication-Accuracy Trade-offs at Scale

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How do different aggregation strategies in federated learning influence the trade-off between communication overhead and detection accuracy when scaling to heterogeneous IoT device networks. This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do different aggregation strategies in federated learning influence the trade-off between communication overhead and detection accuracy when scaling to heterogeneous IoT device networks?

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