Compressive Sensing-Based Over-the-Air Federated Learning Robustness Against Adversarial Attacks

This report synthesises findings from 5 peer-reviewed papers addressing the following research question: How does the robustness of compressive sensing-based OTA-FL compare to traditional FL methods against adversarial attacks in terms of model accuracy and convergence rate, evaluated on the LEAF. Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ML approaches rely on centralized methods, where data. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the robustness of compressive sensing-based OTA-FL compare to traditional FL methods against adversarial attacks in terms of model accuracy and convergence rate, evaluated on the LEAF benchmark datasets?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.