Federated Learning Aggregation Strategies for Non-IID Data in Massive MIMO Systems

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How do different federated learning aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) perform in terms of robustness to non-IID data distributions and model alignment when integrated with. Over-the-air federated learning (OTA-FL) is an emerging technique to reduce the computation and communication overload at the PS caused by the orthogonal transmissions of the model updates in conventional federated learning (FL). This reduction is achieved at the expense of. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do different federated learning aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) perform in terms of robustness to non-IID data distributions and model alignment when integrated with compressive sensing over massive MIMO systems, evaluated using cross-domain benchmark datasets?

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