Abstract The Internet of Things (IoT) has grown exponentially since its inception, combining
many components and systems to enable seamless connectivity and automation. However …

Collaborative mobile edge computing enables resource-constrained edge facilities to work
cooperatively for computation-intensive tasks. However, as the number of tasks demanded …

Convolutional Neural Networks (CNNs) have been widely applied in various edge
computing devices based on intelligent sensors. However, due to the high computational …

Big artificial intelligence (AI) models have emerged as a crucial element in various intelligent
applications at the edge, such as voice assistants in smart homes and autonomous robotics …

The traditional synchronous federated learning framework ensures global model
consistency and accuracy. However, it is limited by the computational power differences …

The late 1990s saw the rise of the edge computing network paradigm, as well as an
increase in the number of IoT de-vices. This concept is viewed as a link between cloud …

Mobile edge processing is a cutting-edge technique that addresses the limitations of mobile
devices by enabling users to offload computational tasks to edge servers, rather than relying …

The development of Industrial Internet of Things (IIoT) applications presents a critical
challenge in terms of latency limitation, particularly considering the limited availability of …

Decentralized coded caching (DCC) is promising to relieve the load pressure of the
networks (ie, reducing the delivery rate) by creating multicast opportunities for a group of …

尽管算力网络可以通过网络连接并灵活地调度计算资源, 但在实际落地应用中仍然缺乏智能性和
灵活性. 针对算力网络亟须提高智能化水平的问题, 首先简要引出了算网自智的背景和概念; …