AMTA: An Innovative Privacy‐Aware Adaptive Transformer for Real‐Time Multimodal Data Fusion

ABSTRACT In this research, we introduce AMTA, an innovative adaptive multimodal transformer algorithm that transforms concurrent data integration in real‐time. This flexibility aids AMTA in optimizing contextual understanding and assuming the forefront of multimodal representation learning using adaptive attention mechanisms that dynamically balance the attention assigned to various modalities like text, images, or sensor feeds according to contextual need, unlike fixed modalities in older frameworks. AMTA is widely accurate (92.4%) and fast (120 ms latency), making it suitable for the time‐sensitive world such as healthcare diagnostics, autonomous vehicle navigation, and so forth. At AMTA, we find a novel way to address privacy and performance, combining federated learning with lightweight encryption for confidential data processing. This makes AMTA ideal for healthcare, defense, and autonomous systems, where it is crucial to handle sensitive data securely. Explainability is a big feature, as SHAP and LIME techniques provide insight into how AMTA makes decisions. Novel adaptive attention visualizations further improve interpretability in the model, allowing the user to better interpret which data modality, such as medical images or patient records, is contributing the most at predicting the outcome. AMTA musters a higher accuracy (92.4% vs. 89.2% vs. 90.1%) and lower latency (120 vs. 150 vs. 140 ms) than both Flamingo and Perceive IO, making it an effective multimodal solution. In future works, further optimizations of AMTA aimed at its adoption in edge devices, among other usages across people‐centric domains such as smart cities, robotics, and intelligent agriculture, will set a gold standard for the development of ethical, scalable AI.