# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 4 Results and Discussion
### Ablation Study on Hybrid Uncertainty Metrics  
The effectiveness of the Hybrid Uncertainty Fusion method, which integrates entropy and KL divergence, was evaluated against entropy-based and KL divergence-based fusion methods on the CREMA-D and MVSA datasets. Performance was assessed using Expected Calibration Error (ECE), F1 score, precision, recall, and Confidence-Stratified Accuracy Low (ConfAcc_Low) over 100 epochs. The results, summarized below and visualized in Figures 7 and 8, demonstrate the Hybrid method's superior performance in classification accuracy, model calibration, and reliability of low-confidence predictions.  
Accuracy-Related Metrics: The experimental results show that for accuracy-related metrics (Accuracy, Precision, F1-Score, Recall), the Hybrid Uncertainty Fusion method consistently outperforms entropy-based and KL-based methods on both CREMA-D (F1-score: 0.676, precision: 0.739, recall: 0.682 vs. entropy-based: 0.594, 0.660, 0.614) and MVSA datasets (F1-score: 0.533, precision: 0.581, recall: 0.581 vs. entropy-based: 0.490, 0.550, 0.537), with accuracy improvements of 9.45% (average) and 6.88% (peak) over entropy-based methods across both datasets. This indicates that by integrating entropy and KL divergence, the hybrid method achieves superior classification performance through balanced modality contributions. These findings reflect the hybrid method's robust predictive accuracy, effectively handling complex audio-visual and text-image interactions with enhanced cross-modal synergy. This performance supports the PAMRL framework's hybrid uncertainty metrics by demonstrating their ability to mitigate modality laziness, ensur  
Figure 6: MIA AUC curves over 100 training epochs for different datasets: (**a**) CREMAD dataset; (**b**) MVSA dataset.  
ing consistent modality integration for reliable multimodal learning in applications like emotion recognition and sentiment analysis.  
**Confidence-Related Metrics**: For confidence-related metrics (ECE, ConfAcc_Low), reveal that the Hybrid Uncertainty Fusion method achieves lower ECE values (0.107 on CREMA-D, 0.111 on MVSA vs. Entropy-based: 0.214, 0.169) and higher ConfAcc_Low (0.374 on CREMA-D, 0.465 on MVSA vs. Entropy-based: 0.292, 0.389), with MVSA showing stability through lower standard deviations (e.g., ECE: 0.036). This indicates that the hybrid method provides superior calibration and reliability in low-confidence predictions, aligning predicted confidence with actual accuracy. These findings reflect the Hybrid method's enhanced robustness in uncertain  
Figure 8: Performance comparison of different fusion methods on MVSA: (**a**) accuracy; (**b**) precision; (**c**) recall; (**d**) F1; (**e**) ECE; (**f**) Low-Confidence Accuracy.  
Figure 7: Performance comparison of different fusion methods on CREMA-D: (**a**) accuracy; (**b**) precision; (**c**) F1; (**d**) recall; (**e**) ECE; (**f**) Low-Confidence Accuracy.  
scenarios, ensuring stable predictions across diverse multimodal data. This performance supports the PAMRL framework's hybrid uncertainty metric by validating their role in addressing modality laziness through improved cross-modal consistency, making PAMRL reliable for privacy-sensitive multimodal applications where prediction stability is critical.