Table 7 Ablation study results for GraphFedAI modules.
Model variant | Accuracy (%) | F1-Score (%) | False positive rate (FPR) | Communication overhead | Observed impact |
|---|---|---|---|---|---|
Full GraphFedAI | 98.7 | 98.4 | 1.2% | Moderate | Optimal performance with all modules integrated |
Without interpolation | 94.9 | 94.1 | 3.8% | Moderate | Drop in accuracy due to missing temporal graph continuity |
Without compression module | 98.2 | 97.8 | 1.6% | High (+ 41.3%) | Increased resource usage; minor accuracy drop |
Centralized GNN (No federated learning) | 95.7 | 94.9 | 3.2% | Very High | Reduced privacy and scalability under non-IID conditions |
MLP instead of GNN | 92.2 | 91.5 | 5.4% | Low | Poor spatial modeling; failed to capture graph structure |
