Motivation
With the breakthroughs in Deep Learning (DL), recent years have witnessed the booming of Artificial Intelligence (AI) applications and services. Driven by the rapid advances in mobile computing and the Artificial Intelligence of Things (AIoT), billions of mobile and IoT devices are connected to the Internet, generating zillion bytes of data at the network edge.
The ability to imbue interconnected devices with intelligence is at the forefront of this technological revolution. In this regard, conventional machine learning techniques have rapidly adapted to various applications in multiple domains. However, DL techniques, though having demonstrated unparalleled performance primarily in Computer Vision and Natural Language Processing fields, are often subjected to significant computation and memory costs as well as massive data requirements. This poses a great challenge to empower devices at the network edge with DL capability. Nowadays, accelerated by the remarkable success of DL and IoT technologies, there is an urgent need to push the DL frontier to the network edge to fully unleash the potential values of big data. The emerging Edge Computing (EC) paradigm provides a promising way to enable this, which leverages distributed computing concepts to push computational loads from the network core to the network edge with the aim to provide faster responses to end users.
Deep Edge Intelligence (DEI) is a combination of DL, AI, EC and IoT. It enables the development and deployment of DL and AI techniques, based on EC, on edge devices, e.g., IoT devices, where the data are generated, aiming to provide AI for every person and every organization at any place. A position paper on the theme of DEI can be found here.
Goals
The goal of this task force is to establish and promote the new research direction of DEI. It will provide a forum for academic and industrial researchers from DL, AI, EC and IoT communities to collaboratively explore promising research topics in DEI and accordingly carry out joint research activities.
Scope
The scope of this task force covers but is not limited to:
• Intelligent EC architectures for DL and AI applications
• Energy, computation, memory and network optimization for DEI applications
• Security and privacy protection for DEI applications
• Quality assurance for DEI applications
• Scalable and adaptable EC using AI
• Efficient edge/fog-cloud integration for federated learning
• Advanced scheduling methods for efficient training, inference, and caching
• Applications of DEI in Industry 4.0, autonomous driving, smart grids, networked robots, Internet of Energy (IoE), etc.
Special Issue
"When Computational Intelligence Meets Edge Intelligence" in IEEE Computational Intelligence Magazine (Impact Factor: 9.0, JCR-Q1, SJR-Q1) (Submission Deadline: 1 January 2024)