Edge AI

Neural Network (FCNN)

                Auto-Encoder (AE)

                Convolutional Neural Network (CNN)

                Generative Adversarial Network (GAN)

                Recurrent Neural Network (RNN)

                Transfer Learning (TL)

        3.2 Deep Reinforcement Learning (DRL)

                Value-based DRL

                Policy-gradient-based DRL

        3.3 Distributed DL Training

        3.4 Potential DL Libraries for Edge

4. Deep Learning Applications on Edge

                4.1 Real-time Video Analytic

                4.2 Autonomous Internet of Vehicles (IoVs)

                4.3 Intelligent Manufacturing

                4.4 Smart Home and City

5. Deep Learning Inference in Edge

                5.1 Optimization of DL Models in Edge

                                General Methods for Model Optimization

                                Model Optimization for Edge Devices

                5.2 Segmentation of DL Models

                5.3 Early Exit of Inference (EEoI)

                5.4 Sharing of DL Computation

6. Edge Computing for Deep Learning

                6.1 Edge Hardware for DL

                                Mobile CPUs and GPUs

        FPGA-based Solutions

6.2 Communication and Computation Modes for Edge DL        Integral Offloading



        Partial Offloading

        Vertical Collaboration

        Horizontal Collaboration

6.3 Tailoring Edge Frameworks for DL

6.4 Performance Evaluation for Edge DL

7. Deep Learning Training at Edge

                7.1 Distributed Training at Edge                7.2 Vanilla Federated Learning at Edge



                7.3 Communication-efficient FL

                7.4 Resource-optimized FL

                7.5 Security-enhanced FL

8. Deep Learning for Optimizing Edge

                8.1 DL for Adaptive Edge Caching

                8.2 DL for Optimizing Edge Task Offloading

                8.3 DL for Edge Management and Maintenance

                                Edge Communication

                                Edge Security

                                Joint Edge Optimization

9. Lessons Learned and Open Challenges

        9.1 More Promising Applications

        9.2 General DL Model for Inference

        9.3 Complete Edge Architecture for DL

        9.4 Practical Training Principles at Edge

        9.5 Deployment and Improvement of Intelligent Edge

 

journal/conference papers and book chapters. Dr. Leung is serving on the editorial boards of the IEEE Transactions on Green Communications and Networking, IEEE Transactions on Cloud Computing, IEEE Access, IEEE Network, and several other journals. He received the IEEE Vancouver Section Centennial Award, 2011 UBC Killam Research Prize, 2017 Canadian Award for Telecommunications Research, and 2018 IEEE TCGCC Distinguished Technical Achievement Recognition Award. He co-authored papers that won the 2017 IEEE ComSoc Fred W. Ellersick Prize, 2017 IEEE Systems Journal Best Paper Award, 2018 IEEE CSIM Best Journal Paper Award, and 2019 IEEE TCGCC Best Journal Paper Award. He is a Fellow of IEEE, the Royal Society of Canada, Canadian Academy of Engineering, and Engineering Institute of Canada. He is named in the current Clarivate Analytics list of "Highly Cited Researchers".

Prof. Dusit Niyato is currently a Professor in the School of Computer Science and Engineering, at Nanyang Technological University, Singapore. He was a Distinguished Lecturer of the IEEE Communications Society for 2016-2017. He was named the 2017, 2018 highly cited researcher in computer science. He is a Fellow of IEEE. His research interests are in the area of Internet of Things (IoT) and network resource pricing.

Xueqiang Yan is currently a technology expert with Wireless Technology Lab at Huawei Technologies. He was a member of technical staff of Bell Labs from 2000 to 2004. From 2004 to 2016 he was a director of Strategy Department of Alcatel-Lucent Shanghai Bell. His current research interests include wireless networking, Internet of Things, edge AI, future mobile network architecture, network convergence and evolution.

Xu Chen is a Full Professor with Sun Yatsen University, Guangzhou, China, and the vice director of National and Local Joint Engineering Laboratory of Digital Home Intera