Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

By Yiwen Han, Xiaofei Wang, Victor C. M. Leung, Dusist Niyato, Xueqiang Yan, Xu Chen. IEEE Communications Surveys and Tutorials, 2019

1,229 citations recorded by the source corpus.

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4cd03cd34e7e94d1b1ee293d5dead8efc24c1a6d

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Citations in the corpus

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “Speciﬁcally, MEC provides a promising solution for deploying edge AI systems in current wireless systems with digital modulation [115].”
A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, background): “cussions from the perspectives of edge intelligence [33], [34].”
Privacy preservation in federated learning: An insightful survey from the GDPR perspective (2020, positive): “g privacy concerns, the trend of decentralised AI has naturally risen which converges the mobile edge computing (MEC) [70] with AI/ML techniques to migrate the intelligence from the cloud to the edge [71]. VOLUME 4, 2016 5 N. Truong et al.: Privacy Preservation in Federated Learning In this regard, ”
A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges (2021, methodology): “Fully Connected Neural Networks can be used for feature extraction (Wang et al., 2020).”
Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, background): “is to capitalize on the storage and computing capabilities of edge servers and end devices to train the machine learning models closer to the origins of the data [33].”
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, positive): “Over time, the performance of AIGC has significantly improved, driven by enhanced models, increased data availability, and greater computational power [12].”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “centric training is relatively reliant on wireless communications [15].”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, methodology): “To achieve the best performance of DL training and reasoning, [15] comprehensively discusses how to design EC architecture with communication, computing power, and energy consumption constraints.”
A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2021, methodology): “For instance, the edge/cloud computing solution for mulitmodal analysis provides an effortless way to create and handle multimodal datasets for training and deploying models [107].”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “[25] Survey on convergence of the intelligent edge and edge intelligence X X X X X X”