Compressive Sensing Integration in Over-the-Air Federated Learning for Massive MIMO Systems

This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How does the integration of compressive sensing techniques with OTA-FL in massive MIMO systems affect the convergence rate and final model accuracy compared to traditional FL methods, as measured on. Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices, each with its own local training data set. In this paper, we present a compressive sensing approach for federated learning over massive. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the integration of compressive sensing techniques with OTA-FL in massive MIMO systems affect the convergence rate and final model accuracy compared to traditional FL methods, as measured on cross-domain benchmark datasets like LEAF or FEMNIST?

Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.