Resource Constrained Edge

Resource-constrained edge computing focuses on efficiently training and deploying machine learning models, particularly deep neural networks (DNNs), on devices with limited computational resources and bandwidth. Current research emphasizes techniques like federated learning, split learning, and model compression (e.g., pruning, parameter reduction) to optimize model performance while minimizing resource consumption. These advancements are crucial for enabling AI applications in diverse edge environments, such as IoT devices and mobile networks, improving both efficiency and privacy. The ultimate goal is to democratize access to powerful AI capabilities while addressing the limitations of resource-scarce edge devices.