Edge Intelligence

Edge intelligence (EI) aims to bring the power of artificial intelligence (AI) closer to data sources, improving latency, bandwidth efficiency, and privacy. Current research focuses on optimizing model architectures (like CNNs, RNNs, and Transformers) for resource-constrained edge devices, employing techniques such as model splitting, quantization, pruning, and federated learning to improve efficiency and address data heterogeneity. This field is significant because it enables the deployment of sophisticated AI applications in diverse contexts, from mobile devices to industrial IoT networks, impacting various sectors through improved responsiveness and reduced reliance on centralized cloud infrastructure.