Abstract
To identify existing challenges and circumvent potential misleading directions, we briefly introduce the potential scenario of “AI Application on Edge,” and separately discuss open issues related to four enabling technologies that we focus on, i.e., “AI Inference in Edge,” “Edge Computing for AI,” “AI Training at Edge,” and “AI for Optimizing Edge.”
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
M.P. Ðurišić, Z. Tafa, G. Dimić, et al., A survey of military applications of wireless sensor networks, in 2012 Mediterranean Conference on Embedded Computing (MECO) (2012), pp. 196–199
J. Jiang, G. Ananthanarayanan, P. Bodik, S. Sen, I. Stoica, Chameleon: scalable adaptation of video analytics, in Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication (SIGCOMM 2018) (2018), pp. 253–266
L.N. Huynh, Y. Lee, R.K. Balan, DeepMon: mobile GPU-based deep learning framework for continuous vision applications, in Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys 2017) (2017), pp. 82–95
X. Ran, H. Chen, X. Zhu, Z. Liu, J. Chen, DeepDecision: a mobile deep learning framework for edge video analytics, in 2018 IEEE Conference on Computer Communications (INFOCOM 2018) (2018), pp. 1421–1429
Y. Huang, X. Ma, X. Fan et al., When deep learning meets edge computing, in IEEE 25th International Conference on Network Protocols (ICNP 2017) (2017), pp. 1–2
S. Teerapittayanon et al., BranchyNet: fast inference via early exiting from deep neural networks, in Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016) (2016), pp. 2464–2469
B.A. Mudassar, J.H. Ko, S. Mukhopadhyay, Edge-cloud collaborative processing for intelligent internet of things, in Proceedings of the 55th Annual Design Automation Conference (DAC 2018) (2018), pp. 1–6
J. Ren, Y. Guo, D. Zhang et al., Distributed and efficient object detection in edge computing: challenges and solutions. IEEE Netw. 32(6), 137–143 (2018)
Y. Gan, Y. Zhang, D. Cheng et al., An open-source benchmark suite for microservices and their hardware-software implications for cloud and edge systems, in Proceedings of the Twenty Fourth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2019) (2019)
M. Alam, J. Rufino, J. Ferreira, S.H. Ahmed, N. Shah, Y. Chen, Orchestration of microservices for IoT using docker and edge computing. IEEE Commun. Mag. 56(9), 118–123 (2018)
J. Xu, S. Wang, B. Bhargava, F. Yang, A blockchain-enabled trustless crowd-intelligence ecosystem on mobile edge computing. IEEE Trans. Ind. Inf. 15, 3538–3547 (2019)
Z. Zheng, S. **e, H. Dai et al., An overview of blockchain technology: architecture, consensus, and future trends, in 2017 IEEE International Congress on Big Data (BigData Congress 2017) (2017), pp. 557–564
J.-y. Kim, S.-M. Moon, Blockchain-based edge computing for deep neural network applications, in Proceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications (INTESA 2018) (2018), pp. 53–55
G. Wood, Ethereum: a secure decentralised generalised transaction ledger (2014) [Online]. Available: http://gavwood.com/Paper.pdf
K. Bonawitz, H. Eichner et al., Towards federated learning at scale: system design (2019). Preprint. ar**v:1902.01046
H.B. McMahan, E. Moore, D. Ramage et al., Communication-efficient learning of deep networks from decentralized data, in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017) (2017), pp. 1273–1282
S. Zheng, Q. Meng, T. Wang et al., Asynchronous stochastic gradient descent with delay compensation, in Proceedings of the 34th International Conference on Machine Learning (ICML 2017) (2017), pp. 4120–4129
C. **e, S. Koyejo, I. Gupta, Asynchronous federated optimization (2019). Preprint. ar**v:1903.03934
W. Wu, L. He, W. Lin, RuiMao, S. Jarvis, SAFA: a semi-asynchronous protocol for fast federated learning with low overhead (2019). Preprint. ar**v:1910.01355
T. Nishio, R. Yonetani, Client selection for federated learning with heterogeneous resources in mobile edge (2018). Preprint. ar**v:1804.08333
T. **ng, S.S. Sandha, B. Balaji et al., Enabling edge devices that learn from each other: cross modal training for activity recognition, in Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking (EdgeSys 2018) (2018), pp. 37–42
R. Sharma, S. Biookaghazadeh et al., Are existing knowledge transfer techniques effective for deep learning on edge devices? in Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2018) (2018), pp. 15–16
J. Yoon, P. Liu, S. Banerjee, Low-cost video transcoding at the wireless edge, in 2016 IEEE/ACM Symposium on Edge Computing (SEC 2016) (2016), pp. 129–141
N. Kato et al., The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wireless Commun. 24(3), 146–153 (2017)
Z.M. Fadlullah, F. Tang, B. Mao et al., State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun. Surveys Tuts. 19(4), 2432–2455 (2017). Fourthquarter
J. Foerster, I.A. Assael et al., Learning to communicate with deep multi-agent reinforcement learning, in Advances in Neural Information Processing Systems 29 (NeurIPS 2016) (2016), pp. 2137–2145
S. Omidshafiei, J. Pazis, C. Amato et al., Deep decentralized multi-task multi-agent reinforcement learning under partial observability, in Proceedings of the 34th International Conference on Machine Learning (ICML 2017) (2017), pp. 2681–2690
R. Lowe, Y. WU et al., Multi-agent actor-critic for mixed cooperative-competitive environments, in Advances in Neural Information Processing Systems 30 (NeurIPS 2017) (2017), pp. 6379–6390
J. Zhou, G. Cui et al., Graph neural networks: a review of methods and applications (2018). Preprint. ar**v:1812.08434
Z. Zhang, P. Cui, W. Zhu, Deep learning on graphs: a survey (2018). Preprint. ar**v:1812.04202
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Wang, X., Han, Y., Leung, V.C.M., Niyato, D., Yan, X., Chen, X. (2020). Lessons Learned and Open Challenges. In: Edge AI. Springer, Singapore. https://doi.org/10.1007/978-981-15-6186-3_9
Download citation
DOI: https://doi.org/10.1007/978-981-15-6186-3_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6185-6
Online ISBN: 978-981-15-6186-3
eBook Packages: Computer ScienceComputer Science (R0)