Sustainable AI: Sparse Backpropagation for Low-Carbon On-Device Training

Deep learning's growing computational demands have led to considerable energy consumption and carbon emissions. In this paper, we explore on-device learning with sparse backpropagation as a means to improve training energy efficiency. We apply this approach to several CNN architectures (ResNet-18, DenseNet-121, GoogleNet, and MobileNet-V2) trained on popular vision datasets (CIFAR-10/100 and Flowers-102) using an NVIDIA Jetson AGX Orin (64GB) edge device. We present a sparse training algorithm that updates only a fraction of model parameters per iteration, reducing computation in the backward pass. We integrate the CodeCarbon library to measure energy use and associated CO₂ emissions. Experimental results show that sparse backpropagation can maintain model accuracy within 1-2% of standard training while reducing energy usage and carbon emissions by up to 30-40%. We analyze trade-offs between accuracy, speed, and sustainability and discuss deployment strategies for energy-efficient on-device learning. These findings demonstrate a practical step toward "Green AI"by cutting the carbon footprint of deep learning without significantly compromising performance.