# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 5 Conclusions  
In this paper, we propose Privacy-Preserving Alternating Multimodal Representation Learning (PAMRL), a framework addressing modality laziness and privacy concerns through two key innovations: a mixed uncertainty metric combining Kullback-Leibler divergence (KL divergence) and entropy to dynamically adjust modality weights during fusion, enhancing prediction accuracy and model robustness, and a privacy-preserving training strategy utilizing Differential Privacy Stochastic Gradient Descent (DP-SGD) applied selectively to unimodal encoders, ensuring sensitive data protection while preserving flexibility in the shared fusion head for efficient cross-modal integration. By effectively mitigating modality laziness and strengthening security, PAMRL balances efficiency and data protection, achieving a slight 2.5% accuracy drop due to differential privacy while reducing membership inference attack (MIA) success by 18%, making it well-suited for practical multimodal learning applications. This framework effectively mitigates modality laziness and strengthens security for practical multimodal learning applications.  
This work has some limitations that warrant further attention in future work. We summarized as follows. Despite the achievements of the PAMRL framework, challenges persist, including a privacy-performance trade-off where high privacy noise reduces CREMA-D's accuracy by approximately 10% under elevated noise levels, manual tuning of privacy parameters like gradient clipping and noise standard deviation that lacks adaptability, and computational overhead from differential privacy that limits scalability and efficiency on large datasets. Future research will focus on developing efficient differential privacy techniques, such as adaptive noise injection, to minimize performance loss while preserving privacy and designing adaptive parameter adjustment algorithms based on task or data characteristics to enhance the framework's versatility and usability.  
Conceptualization, Z.S. and C.L.; Data curation, Y.H.; Formal analysis, Y.H. and A.Z.; Investigation, R.L. and X.L.; Software, Y.H. and A.Z.; Supervision, Z.S. and C.L.; Writing--original draft, L.J. and Y.H.; Writing--review and editing, Z.S., C.L., J.W., and R.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Key R&D Program of China (No. 2022 YFB3104100), in part by the Major Research plan of the National Natural Science Foundation of China, grant number (No. 92167203); in part by the National Natural Science Foundation of China, grant number (No. 62472114, 62002077); in part by Guangdong Basic and Applied Basic Research Foundation, grant number (No. 2024A1515011492); and in part by Guangzhou Municipal Bureau of Education Higher Education Research Project, grant number (No. 2024312190).  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. The CREMA-D dataset can be found here: [[https://github.com/CheyneyComputerScience/CREMA-D](https://github.com/CheyneyComputerScience/CREMA-D)]([https://github.com/CheyneyComputerScience/CREMA-D](https://github.com/CheyneyComputerScience/CREMA-D)) (accessed on 15 February 2025). The MVSA dataset can be found here: [[https://www.kaggle.com/datasets/vincemarcs/mvSAsingle](https://www.kaggle.com/datasets/vincemarcs/mvSAsingle)]([https://www.kaggle.com/datasets/vincemarcs/mvSAsingle](https://www.kaggle.com/datasets/vincemarcs/mvSAsingle)) (accessed on 15 February 2025).  
The authors declare no conflicts of interest.