DA-Distill: Dual-Alignment Distillation for Multimodal Knowledge Transfer on Edge Devices

The growing demand for multimodal functionalities in consumer electronics has driven the widespread adoption of multimodal pre-trained models, such as CLIP, enhancing human-computer interactions on edge devices through adaptive and context-aware functionalities. However, deep neural architectures impose significant computational complexity, storage demands, and data privacy challenges that limit traditional fine-tuning methods at the edge. To overcome these constraints, we propose an efficient knowledge transfer framework comprising two main modules: a data filtering module employing a combined confidence-driven and random sampling strategy to select representative samples while ensuring privacy compliance, and a knowledge distillation module that explicitly transfers multimodal representations via contrastive learning. Specifically, we construct bidirectional cross-modal contrastive distributions and minimize their Kullback-Leibler divergence to achieve representation alignment between the old and new models. Experimental results indicate that our method significantly outperforms conventional fine-tuning, achieving more than 1.5% accuracy improvement using only 5% of the original training data. Additionally, we identify optimal data selection strategies tailored to different data availability scenarios, demonstrating the practical effectiveness and flexibility of our approach for resource-constrained edge computing applications.