# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 1 Introduction  
Multimodal learning is a significant research direction in artificial intelligence, enhancing semantic representations and model performance in complex tasks by integrating diverse modalities such as vision, audio, and language [1]. For instance, in sentiment analysis, combining facial expressions, speech intonation, and text enables precise emotion recognition [2]; in autonomous driving, fusing images, LiDAR, and voice commands improves environmental perception and decision-making reliability [3]. Recent advancements in transformer-based architectures, such as vision transformers and multimodal transformers, have become foundational to multimodal learning due to their powerful feature extraction and cross-modal integration capabilities, significantly advancing the field [4]. [PERSON] et al. [1] note that multimodal models exhibit strong robustness and adaptability in handling high-dimensional, heterogeneous data, particularly in noisy orincomplete real-world scenarios. Meanwhile, several critical challenges limit their practical adoption. Multimodal learning is hindered by two major issues: modality laziness and privacy leakage. Modality laziness occurs when models over-rely on dominant modalities (e.g., images) while neglecting others (e.g., text or audio), undermining synergy and causing performance degradation in scenarios with missing or noisy modalities [3]. Additionally, models processing sensitive data, such as medical images or voice recordings, are vulnerable to privacy leakage through gradient updates and susceptible to membership inference attacks, posing significant barriers to deployment in privacy-sensitive domains like healthcare and finance [5]. To address the issue of modality laziness and privacy leakage, numerous researchers have proposed various methods. For instance, [PERSON] et al. [6] introduced the Multimodal Learning with Alternating Unimodal Adaptation (MLA) framework, which mitigates modality laziness by alternately optimizing modality-specific encoders and employs a shared head to capture cross-modal semantic relationships. During inference, MLA utilizes an uncertainty-based dynamic fusion mechanism to enhance integration accuracy [7]. MLA outperforms traditional fusion methods on datasets such as CREMA-D [2] for sentiment analysis and MVSA [8] for image-text classification, particularly in modality-imbalanced scenarios [9]. However, MLA's entropy-based uncertainty metric solely measures prediction confidence, failing to ensure accuracy or reliability, which may lead to \"confidently incorrect\" predictions in noisy or modality-inconsistent settings [10; 11]. Furthermore, MLA lacks privacy safeguards, as frequent gradient updates render it vulnerable to membership inference attacks, limiting its applicability to sensitive data scenarios [5; 12]. These shortcomings underscore the urgent need for a more robust and secure multimodal learning framework. In this work, we propose the Privacy-Preserving Alternating Multimodal Representation Learning (PAMRL) framework, a sophisticated solution designed to tackle the inherent challenges of modality laziness, suboptimal uncertainty quantification, and privacy vulnerabilities in multimodal learning systems. PAMRL integrates a hybrid uncertainty metric, which leverages the complementary strengths of Kullback-Leibler (KL) divergence and entropy to dynamically evaluate modality quality, thereby enhancing modality fusion, ensuring prediction robustness, and promoting consistent cross-modal semantic alignment across diverse data types such as audio-visual inputs for emotion recognition and text-image pairs for sentiment analysis. Additionally, PAMRL employs differential privacy through an innovative alternating training strategy, utilizing Differential Privacy Stochastic Gradient Descent (DP-SGD) with gradient clipping and calibrated noise injection applied selectively to unimodal encoders, safeguarding sensitive data while maintaining the shared representation head's efficiency for seamless cross-modal integration.