Edge Assisted

Edge-assisted computing focuses on leveraging the processing power of edge devices (like smartphones or edge servers) to improve the efficiency and privacy of machine learning tasks, particularly in resource-constrained environments like the Internet of Things (IoT) or autonomous driving. Current research emphasizes the development of novel architectures, such as graph neural networks and federated learning frameworks, often incorporating techniques like uncertainty quantification and privacy-preserving mechanisms. This approach is significant for enabling the deployment of complex AI models in real-world applications while addressing challenges related to data privacy, communication bandwidth, and computational limitations.