Optimizing Computational Efficiency and Model Robustness through Adaptive Deep Learning Pipelines with Layerwise Gradient Modulation

Abstract

The exponential growth in model complexity has imposed a dual challenge of maintaining computational efficiency while ensuring robustness in deep learning systems. This paper presents an adaptive pipeline framework that integrates layerwise gradient modulation (LGM) to address these issues. By dynamically adjusting gradient scaling across layers based on performance feedback, we achieve notable improvements in convergence stability and resource utilization. Experimental evaluations across convolutional neural networks (CNNs) and transformer architectures demonstrate up to 23% faster convergence and a 15–21% improvement in robustness to adversarial perturbations. This work paves the way for more efficient and fault-tolerant deep learning systems.