FedAvg FedProx and SCAFFOLD with Compressive Sensing in OTA-FL for CIFAR-10 Inference Efficiency

This report synthesises findings from 5 peer-reviewed papers addressing the following research question: How does the combination of FedAvg, FedProx, and SCAFFOLD aggregation strategies with compressive sensing in massive MIMO-enabled OTA-FL affect inference efficiency (latency, throughput) on CIFAR-10. Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency. 11 claims were extracted from source literature; 11 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the combination of FedAvg, FedProx, and SCAFFOLD aggregation strategies with compressive sensing in massive MIMO-enabled OTA-FL affect inference efficiency (latency, throughput) on CIFAR-10 compared to standard FL under non-IID data distributions?

Autonomous literature synthesis. Automated review score: 9.2/10. Full text and citation available at Assignee Research.