Table 1.
Summary of related works on IoT security approaches.
| Reference | Focus area | Approach | Limitations |
|---|---|---|---|
| Lu et al.5 | Federated learning for industrial IoT | Blockchain-integrated FL for privacy | Limited attack diversity and interpretability |
| Kumar et al.6 | Secure FL in consumer IoT | Blockchain + encryption-based FL | Poor adaptability in dynamic IoT environments |
| Issa et al.7 | Federated learning & blockchain survey | A comprehensive review of FL + blockchain | Lacks unified edge deployment models |
| Wang et al.8 | Clustered FL on non-IID data | FedAvg with clustering | No integration with access control or anomaly detection |
| Mills et al.9 | FL efficiency in wireless edge | Comm-efficient FL for IoT | Limited trust and privacy mechanisms |
| Khoei et al.10 | DL challenges in IoT | Survey of DL models for Edge devices | No practical implementation or access control focus |
| Thakur et al.11 | Secure smart homes | Edge-AI for home IoT | Lack of trust-based access control and FL integration |
| Zhang et al.20 | Federated learning in healthcare IoT | Lightweight FL with local privacy mechanisms | No trust-based access control or real-time anomaly detection |
| Ahmed et al.21 | Fog-based IoT access control | Policy-aware fog security framework | Lacks federated training and dynamic behavioral analysis |
| Lin et al.22 | Edge AI with blockchain | Secure edge framework with encryption layers | No evaluation with heterogeneous edge environments or FL |