The potential held by the gargantuan volumes of data being generated across networks worldwide has been truly unlocked by machine learning techniques and more recently Deep Learning. The advantages offered by the latter have seen it rapidly becoming a framework of choice for various applications. However, the centralization of computational resources and the need for data aggregation have long been limiting factors in the democratization of Deep Learning applications. Edge Computing is an emerging paradigm that aims to utilize the hitherto untapped processing resources available at the network periphery. Edge Intelligence (EI) has quickly emerged as a powerful alternative to enable learning using the concepts of Edge Computing. Deep Learning-based Edge Intelligence or Deep Edge Intelligence (DEI) lies in this rapidly evolving domain. In this article, we provide an overview of the major constraints in operationalizing DEI. The major research avenues in DEI have been consolidated under Federated Learning, Distributed Computation, Compression Schemes and Conditional Computation. We also present some of the prevalent challenges and highlight prospective research avenues.
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions
Pennsylvania State University（宾夕法尼亚州立大学）；Fudan University（复旦大学）
联合学习（Federated Learning，FL）和分裂学习（Split Learning，SL）是两种新兴的协作学习方法，可以极大地促进物联网（Internet of Things，IoT）中的泛在智能。联邦学习使使用私有数据本地训练的机器学习（ML）模型能够聚合成全局模型。拆分学习允许ML模型的不同部分在学习框架中的不同工作者上进行协作训练。联邦学习和分裂学习各有其独特的优势和各自的局限性，可以相互补充，共同实现物联网中的泛在智能。因此，联邦学习和分裂学习的结合成为近年来一个活跃的研究领域，引起了广泛的兴趣。在本文中，我们回顾了联邦学习和分离学习的最新发展，并对在基于边缘计算的物联网环境中结合这两种学习方法的最新技术进行了调查。我们还确定了一些开放的问题，并讨论了在这一领域的未来研究的可能方向，希望进一步引起研究界对这一新兴领域的兴趣。
School of Cyber Engineering, Xidian University（西安电子科技大学电子工程学院）；Chinese Academy of Sciences（中国科学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Cyberspace Institute of Advanced Technology, Guangzhou University（广州大学空天信息研究院）
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
Singapore University of Technology and Design（新加坡科技设计大学）；Kyung Hee University（庆熙大学）；Zhejiang University/University of Illinois Urbana-Champaign Institute（浙江大学伊利诺伊大学厄巴纳香槟校区联合学院）；Zhejiang University（浙江大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI
IISER Berhampur（贝汉布尔印度科学教育与研究学院）；TU Wien（维也纳工业大学）；University of Helsinki（赫尔辛基大学）；University of Prishtina（普里什蒂纳大学）；University of Oulu（奥卢大学）；University of Hyderabad（海得拉巴大学）；Stockholm University（斯德哥尔摩大学）
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence
Sun Yat-sen University（中山大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）；Midea Group（美的集团）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Peng Cheng Laboratory（鹏城实验室）；Terminus Group（特斯联集团）；The University of Waterloo（滑铁卢大学）
Bringing AI To Edge: From Deep Learning's Perspective
HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University（南洋理工大学惠普-南大数字制造企业实验室）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Innovations and Experiences–Business Personal Systems, HP Inc.（惠普公司创新与体验-个人系统业务部）
Accelerating Edge Intelligence via Integrated Sensing and Communication
Southern University of Science and Technology（南方科技大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
Edge Intelligence: Architectures, Challenges, and Applications
University of Helsinki（赫尔辛基大学）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；Hong Kong University of Science and Technology（香港科技大学）；Huazhong University of Science and Technology（华中科技大学）；University of Cambridge（剑桥大学）
Huazhong University of Science and Technology（华中科技大学）；Xidian University（西安电子科技大学）；Virginia Tech（弗吉尼亚理工大学）；Princeton University（普林斯顿大学）
边缘智能，也称为边缘原生人工智能（AI），是一种新兴的技术框架，专注于AI、通信网络和移动边缘计算的无缝集成。它被认为是现有5G网络中缺少的关键组件之一，并被广泛认为是未来无线6G蜂窝系统最受欢迎的功能之一。在本文中，我们确定了6G中边缘原生AI的关键要求和挑战。在\blu{demonstrate the potential performance improvement that can be achieved by automatic data learning and synthesizing at the edge of the network}中引入了一种基于自监督生成对抗网（GANs）的自学习结构。我们在通过5G网络连接的大学校园穿梭系统中评估了我们提出的自学习架构的性能。我们的研究结果表明，该架构具有识别和分类边缘计算网络中出现的未知服务的潜力。还讨论了支持自学习的6G边缘智能的未来趋势和关键研究问题。
University of Oulu（奥卢大学）；Capobianco（卡波比安科公司）；Huawei（华为）；TU Delft（代尔夫特理工大学）；University of Vigo（维戈大学）；VTT Technical Research Centre of Finland（芬兰技术研究中心）；Poznan University of Technology（波兹南理工大学）；University of Helsinki（赫尔辛基大学）；Pengcheng Laboratory（鹏城实验室）
Integrated Sensing-Communication-Computation for Edge Artificial Intelligence
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京邮电大学信息与通信工程学院）；Tsinghua University（清华大学）
EI-MTD:Moving Target Defense for Edge Intelligence against Adversarial Attacks
Zhejiang University of Science and Technology（浙江科技大学）；Zhejiang University（浙江大学）；Guangzhou University（广州大学）；Hangzhou Hikvision Digital Technology Co., Ltd.（杭州海康威视数字技术股份有限公司）
Distributed Threat Intelligence at the Edge Devices: A Large Language Model-Driven Approach
Southern Illinois University（南伊利诺伊大学）；School of Computing（计算机学院）；Center for Research and Education in AI and Cybersecurity (CARE-AI-C)（人工智能与网络安全研究教育中心）
Design of a dynamic and self adapting system, supported with artificial intelligence, machine learning and real time intelligence for predictive cyber risk analytics in extreme environments, cyber risk in the colonisation of Mars
University of Oxford（牛津大学）；Cisco Research Centre（思科研究中心）；Cardiff University（卡迪夫大学）；University of Warwick（华威大学）
College of Computer Science and Technology（计算机科学与技术学院）；State Key Laboratory of Brain-machine Intelligence（脑机智能国家重点实验室）；Zhejiang University（浙江大学）；School of Software Technology（软件学院）；School of Computer Science（计算机学院）；Fudan University（复旦大学）；Institute of Data Science（数据科学研究院）；National University of Singapore（新加坡国立大学）；First Affiliated Hospital（第一附属医院）；Zhejiang University School of Medicine（浙江大学医学院）；Distributed Systems Group（分布式系统组）；Technische Universität Wien（维也纳技术大学）；ICREA Barcelona（巴塞罗那ICREA）；The University of Sydney（悉尼大学）
From Digital Twins to World Models:Opportunities, Challenges, and Applications for Mobile Edge General Intelligence
State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, College of Computer, Northwest University（高级网络与智能信息服务省州联合工程研究中心，计算机学院，西北大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；Automation of School, Guangdong University of Technology（自动化学院，广东工业大学）
Research on Edge Detection of LiDAR Images Based on Artificial Intelligence Technology
University of Houston（休斯顿大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；The University of Chicago（芝加哥大学）；Columbia University（哥伦比亚大学）；University of Electronic Science and Technology of China（电子科技大学）
