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On-demand Privacy Preservation for Cost-Efficient Edge Intelligence Model Training

  • Zhi ZhouEmail author
  • Xu Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11821)

Abstract

With the advancement of Internet-of-Things (IoT), enormous IoT data are generated at the network edge, incurring an urgent need to push the frontiers of artificial intelligence (AI) to network edge so as to fully unleash the potential of the IoT big data. To match this trend, edge intelligence—an emerging paradigm that hosts AI applications at the network edge—is being recognized as a promising solution. While pilot efforts on edge intelligence have mostly focused on facilitating efficient model inference at the network edge, the training of edge intelligence model has been greatly overlooked. To bridge this gap, in this paper, we investigate how to coordinate the edge and the cloud to train edge intelligence model, with the goal of simultaneously optimizing the resource cost and preserving data privacy in an on-demand manner. Leveraging Lyapunov optimization theory, we design and analyze a cost-efficient optimization framework to make online decisions on training data scheduling to balance the tradeoff between cost efficiency and privacy preservation. With rigorous theoretical analysis, we verify the efficacy of the presented framework.

References

  1. 1.
    Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE IoT J. 3(5), 637–646 (2016) Google Scholar
  2. 2.
    Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE (2019)Google Scholar
  3. 3.
    Li, E., Zhou, Z., Chen, X.: Edge intelligence: on-demand deep learning model co-inference with device-edge synergy. In: Proceedings of ACM MECOMM (2018)Google Scholar
  4. 4.
    Kang, Y., et al.: Neurosurgeon: collaborative intelligence between the cloud and mobile edge. In: Proceedings of ACM ASPLOS (2017)Google Scholar
  5. 5.
    Liu, S., Lin, Y., Zhou, Z., Nan, K., Liu, H., Du, J.: On-demand deep model compression for mobile devices: a usage-driven model selection framework. In: Proceedings of ACM Mobisys (2018)Google Scholar
  6. 6.
    Guo, P., Hu, B., Li, R., Hu, W.: FoggyCache: cross-device approximate computation reuse. In: Proceedings of ACM Mobicom (2018)Google Scholar
  7. 7.
    Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutor. 19, 2322–2358 (2017)CrossRefGoogle Scholar
  8. 8.
    Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE IoT J. 5, 450–465 (2017)Google Scholar
  9. 9.
    Zhou, Z., Wu, Q., Chen, X.: Online orchestration of cross-edge service function chaining for cost-efficient edge computing. IEEE J. Sel. Areas Commun. 37, 1866–1880 (2019)CrossRefGoogle Scholar
  10. 10.
    Neely, M.J.: Stochastic Network Optimization with Application to Communication and Queueing Systems. Morgan & Claypool, San Rafael (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

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