This report synthesises findings from 7 peer-reviewed papers addressing the following research question: What is the impact of different federated learning aggregation strategies (FedAvg, FedProx, SCAFFOLD) on model alignment and robustness to non-IID data distributions when combined with compressive. 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: What is the impact of different federated learning aggregation strategies (FedAvg, FedProx, SCAFFOLD) on model alignment and robustness to non-IID data distributions when combined with compressive sensing in massive MIMO-enabled OTA-FL, evaluated using metrics like test accuracy and F1-score on datasets such as CIFAR-10 or Shakespeare?Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.