정보처리학회논문지:컴퓨터 및 통신 시스템 (KIPS Transactions on Computer and Communication Systems)

  • 제9권12호
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  • Pages.291-306
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  • 2020
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  • 2287-5891(pISSN)
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  • 2734-049X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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딥러닝을 사용하는 IoT빅데이터 인프라에 필요한 DNA 기술을 위한 분산 엣지 컴퓨팅기술 리뷰

Distributed Edge Computing for DNA-Based Intelligent Services and Applications: A Review

  • 투고 : 2020.09.18
  • 심사 : 2020.12.09
  • 발행 : 2020.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

오늘날 데이터 네트워크 AI (DNA) 기반 지능형 서비스 및 애플리케이션은 비즈니스의 삶의 질과 생산성을 향상시키는 새로운 차원의 서비스를 제공하는 것이 현실이 되었다. 인공지능(AI)은 IoT 데이터(IoT 장치에서 수집한 데이터)의 가치를 높이며, 사물 인터넷(IoT)은 AI의 학습 및 지능 기능을 촉진한다. 딥러닝을 사용하여 대량의 IoT 데이터에서 실시간으로 인사이트를 추출하려면 데이터가 생성되는 IoT 단말 장치에서의 처리능력이 필요하다. 그러나 딥러닝에는 IoT 최종 장치에서 사용할 수 없는 상당 수의 컴퓨팅 리소스가 필요하다. 이러한 문제는 처리를 위해 IoT 최종 장치에서 클라우드 데이터 센터로 대량의 데이터를 전송함으로써 해결되었다. 그러나 IoT 빅 데이터를 클라우드로 전송하면 엄청나게 높은 전송 지연과 주요 관심사인 개인 정보 보호 문제가 발생한다. 분산 컴퓨팅 노드가 IoT 최종 장치 가까이에 배치되는 엣지 컴퓨팅은 높은 계산 및 짧은 지연 시간 요구 사항을 충족하고 사용자의 개인 정보를 보호하는 실행 가능한 솔루션이다. 본 논문에서는 엣지 컴퓨팅 내에서 딥러닝을 활용하여 IoT 최종 장치에서 생성된 IoT 빅 데이터의 잠재력을 발휘하는 현재 상태에 대한 포괄적인 검토를 제공한다. 우리는 이것이 DNA 기반 지능형 서비스 및 애플리케이션 개발에 기여할 것이라고 본다. 엣지 컴퓨팅 플랫폼의 여러 노드에서 딥러닝 모델의 다양한 분산 교육 및 추론 아키텍처를 설명하고 엣지 컴퓨팅 환경과 네트워크 엣지에서 딥러닝이 유용할 수 있는 다양한 애플리케이션 도메인에서 딥러닝의 다양한 개인 정보 보호 접근 방식을 제공한다. 마지막으로 엣지 컴퓨팅 내에서 딥러닝을 활용하는 열린 문제와 과제에 대해 설명한다.

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

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과제정보

이 논문은 한국연구재단 이공분야기초연구 과제의 지원으로 수행되었음(2019063128).

참고문헌

  1. International Data Corporation (IDC) [Internet], https://www.idc.com/getdoc.jsp?containerId=prUS45213219#:-:text=A%20new%20forecast%20from%20International,these%20devices%20will%20also%20grow.
  2. L. R. Zheng, H. Tenhunen, and Z. Zou, "Smart Electronic Systems: Heterogeneous Integration of Silicon and Printed Electronics," John Wiley & Sons, 2018.
  3. M. Anandhalli and V. P Baligar, "A novel approach in real-time vehicle detection and tracking using Raspberry Pi," Alexandria Engineering Journal, Vol.57, Issue 3, pp.1597-1607, 2018. https://doi.org/10.1016/j.aej.2017.06.008
  4. M. Syafrudin, G. Alfian, N. L. Fitriyani, and J. Rhee, "Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing," Sensors, Vol.18, Issue 9, pp.2946, 2018. https://doi.org/10.3390/s18092946
  5. H. Khelifi, S. Luo, B. Nour et al., "Bringing deep learning at the edge of information-centric internet of things," IEEE Communications Letters, Vol.23, Issue 1, pp.52-55, 2019. https://doi.org/10.1109/LCOMM.2018.2875978
  6. Y. Liu, C. Yang, L. Jiang, and Y. Zhang, "Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities," IEEE Network, Vol.33, Issue 2, pp.111-117, 2019. https://doi.org/10.1109/mnet.2019.1800254
  7. M. Merenda, F. G. Praticò, R. Fedele, R. Carotenuto, and F. G. D. "A Real-Time Decision Platform for the Management of Structures and Infrastructures," Electronics, Vol.8, Issue 10, pp.1180, 2019. https://doi.org/10.3390/electronics8101180
  8. S. E. Bibri, "The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability," Sustainable Cities and Society, Vol.38, pp.230-253, April, 2018. https://doi.org/10.1016/j.scs.2017.12.034
  9. C. Ieracitano, N. Mammone, A. Hussain and F. C. Morabito, "A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia," Neural Networks, Vol.123, pp.176-190, March, 2020. https://doi.org/10.1016/j.neunet.2019.12.006
  10. A. Rajkomar, J. Dean, and I. Kohane, "Machine learning in medicine," The New England Journal of Medicine, Vol.380, pp.1347-1358, 2019. https://doi.org/10.1056/NEJMra1814259
  11. K. Y. Ngiam and I. W. Khor, "Big data and machine learning algorithms for health-care delivery," The Lancet Oncology, Vol.20, Issue 5, pp.e262-e273, 2019. https://doi.org/10.1016/S1470-2045(19)30149-4
  12. J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Networks, Vol.61, pp.85-117, Jan. 2015. https://doi.org/10.1016/j.neunet.2014.09.003
  13. B. Arshad, R. Ogie, J. Barthelemy, B. Pradhan, N. Verstaevel, and P. Perez, "Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review," Sensors, Vol.19, Issue 22, pp.5012, 2019. https://doi.org/10.3390/s19225012
  14. B. Ravandi and I. Papapanagiotou, "A self-learning scheduling in cloud software defined block storage," in 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp.415-422, Jun. 2017.
  15. Q. Pu, G. Ananthanarayanan, P. Bodik, S. Kandula, A. Akella, P. Bahl, and I. Stoica, "Low latency geo-distributed data analytics," in Proc. Of ACM SIGCOMM, 2015.
  16. Y. Sahni, J. Cao, and L. Yang, "Data-aware task allocation for achieving low latency in collaborative edge computing," IEEE Internet of Things Journal, Vol.6, Issue 2, pp.3512- 3524, 2019. https://doi.org/10.1109/JIOT.2018.2886757
  17. J. Ren, Y. He, G. Huang, G. Yu, Y. Cai, and Z. Zhang, "An edge-computing based architecture for mobile augmented reality," IEEE Network, Vol.33, Issue 4, pp.162-169, 2019. https://doi.org/10.1109/MNET.2018.1800132
  18. M. Satyanarayanan, "The emergence of edge computing," Computer (Long. Beach. Calif)., Vol.50, Issue 1, pp.30-39, 2017. https://doi.org/10.1109/MC.2017.9
  19. Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, "A survey on mobile edge computing: The communication perspective," IEEE Communications Surveys & Tutorials, Vol.19, Issue 4, pp.2322-2358, 2017. https://doi.org/10.1109/COMST.2017.2745201
  20. S. Weisong, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge computing: Vision and challenges," IEEE Internet of Things Journal, Vol.3, Issue 5, pp.637-646, 2016. https://doi.org/10.1109/JIOT.2016.2579198
  21. Y. LeCun, Y. Bengio, and G. Hinton. "Deep learning," Nature 521, Vol.7553, pp.436-444, 2015
  22. I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, "Deep Learning," Vol.1. Cambridge, MA, USA: MIT Press, 2016.
  23. S. Verma, Y. Kawamoto, Z. M. Fadlullah, H. Nishiyama, and N. Kato, "A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues," IEEE Communications Surveys and Tutorials, Vol.19, Issue 3, pp.1457-1477, 2017. https://doi.org/10.1109/COMST.2017.2694469
  24. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, "Natural language processing (almost) from scratch," Journal of Machine Learning Research, 12 ARTICLE, pp.2493-2537, 2011.
  25. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Communications of the ACM, Vol.60, Issue 6, pp.84-90, 2017. https://doi.org/10.1145/3065386
  26. M. McClellan, C. Cervello-Pastor, and S. Sallent, "Deep Learning at the Mobile Edge: Opportunities for 5G Networks," Applied Sciences, Vol.10, Issue 4, pp.4735, 2020. https://doi.org/10.3390/app10144735
  27. A. L. Caterini and D. E. Chang, "Recurrent neural networks," Design and Applications, Vol.5, 2018.
  28. P. Agarwal, and M. A. Alam, "Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices," Procedia Computer Science, Vol.167, 2020
  29. E. Li, L. Zeng, Z. Zhou, and X. Chen, "Edge AI: On-Demand Accelerating Deep Neural Network Inference," IEEE Transactions on Wireless Communications, Vol.19, Issue 1, 2019
  30. B. Jacob et al., "Quantization and training of neural networks for efficient integer-arithmetic-only inference," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2204-2713, 2018.
  31. W. Meng et al., "Two-bit networks for deep learning on resource-constrained embedded devices," arXiv preprint arXiv:1701.00485, 2017.
  32. P. Gysel, M. Motamedi, and S. Ghiasi, "Hardware-oriented Approximation of Convolutional Neural Networks," arXiv preprint arXiv:1604.03168, 2016.
  33. Intel Developer Zone [Internet], https://software.intel.com
  34. C. Yang, Z. Yang, A. M. Khattak, L. Yang, W. Zhang, W. Gao, and M. Wang, "Structured Pruning of Convolutional Neural Networks via L1 Regularization," IEEE Access, Vol.7, pp.106385-106394, 2019. https://doi.org/10.1109/ACCESS.2019.2933032
  35. F. Tung and G. Mori, "Deep Neural Network Compression by In-Parallel Pruning-Quantization," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
  36. H. Hu, R. Peng, Y.-W. Tai, and C.-K. Tang, "Network trimming: A data-driven neuron pruning approach towards efficient deep architectures," arXiv preprint arXiv: 1607.03250, 2016.
  37. J.-H. Luo, J. Wu, and W. Lin, "Thinet: a filter level pruning method for deep neural net- work compression," Proceedings of the IEEE International Conference on Computer Vision, 2017.
  38. S. Yao, Y, Zhao, A. Zhang, L. Su and T. Abdelzaher, "DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework," Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, pp.1-14, 2017.
  39. T. J. Yang, Y. H. Chen, and V. Sze, "Designing energyefficient convolutional neural networks using energyaware pruning," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5687-5695, 2017.
  40. G. Hinton, O. Vinyals, and J. Dean, "Distilling the Knowledge in a Neural Network," arXiv preprint arXiv:1503.02531, 2015.
  41. M.-C. Wu, C.-T. Chiu, and K.-H. Wu, "Multi-teacher knowledge distillation for compressed video action recognition on deep neural networks," ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp.2202-2206, 2019.
  42. M. Gao, Y. Shen, Q. Li, J. Yan, L. Wan, D. Lin, C. Change Loy, and X. Tang, "An embarrassingly simple approach for knowledge distillation," arXiv preprint arXiv:1812.01819, 2018.
  43. Y. Lin, S. W. Han, H. Mao, Y. Wang, and W. J. Dally, "Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training," arXiv preprint arXiv:1712.01887, 2017.
  44. S. Teerapittayanon, B. McDanel and H.T. Kung, "Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices," 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), IEEE, pp.328-339, 2017.
  45. X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, "In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning," IEEE Network, Vol.33, Issue 5, pp.156-165, 2019. https://doi.org/10.1109/mnet.2019.1800286
  46. S. B. Carlo, M. Touna, D. C. Verma, and A. Cullen, "Edge Computing Architecture for applying AI to IoT," IEEE International Conference on Big Data (Big Data), IEEE, pp.3012-3016, 2017.
  47. S. Sureddy, K. Rashmi, R. Gayathri, and A. S. Nadhan, "Flexible Deep Learning in Edge Computing for Internet of Thinkgs," International Journal of Pure and Applied Mathematics, Vol.119, Issue 10, pp.531-543, 2018
  48. H. Li, K. Ota, and M. Dong, "Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing," IEEE Network, Vol.32, Issue 1, pp.96-101, 2018. https://doi.org/10.1109/MNET.2018.1700202
  49. Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, "Edge intelligence: Paving the last mile of artificial intelligence with edge computing," Proceedings of the IEEE, Vol.107, Issue 8, pp.1738-1762, 2019. https://doi.org/10.1109/JPROC.2019.2918951
  50. J. Chen and X. Ran, "Deep Learning with Edge Computing: A Review," Proceedings of the IEEE, Vol.107, Issue 8, pp. 1655-1674, 2019. https://doi.org/10.1109/JPROC.2019.2921977
  51. M. Merenda, C. Porcaro, and D. Iero, "Edge Machine Learning for AI-Enabled IoT Devices: A Review," Sensors. Vol.20, Issue 9, pp.2533, 2020. https://doi.org/10.3390/s20092533
  52. M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng. Tensorflow, "A system for large-scale machine learning," The 12th USENIX Conference on Operating Systems Design and Implementation({OSDI} 16), 2016.
  53. J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, and A. Y. Ng, "Large scale distributed deep networks," Advances in Neural Information Processing Systems, Vol.25, pp1223-1231, 2012.
  54. R. Mayer and H.-A. Jacobsen, "Scalable Deep Learning on Distributed Infrastructures: Challenges, Techniques, and Tools," ACM Computing Surveys (CSUR), Vol.53, Issue 1, pp.1-37, 2020.
  55. Z. Jia, M. Zaharia, and A. Aiken, "Beyond data and model parallelism for deep neural networks," arXiv preprint arXiv:1807.05358, 2018.
  56. J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, and A. Y. Ng, "Large scale distributed deep networks," Advances in Neural Information Processing Systems, Vol.25, pp.1223-1231, 2012.
  57. J. Konecny, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon, "Federated learning: Strategies for improving communication efficiency," arXiv preprint arXiv:1610.05492, 2016.
  58. J. Konecny, B. McMahan, and D. Ramage, "Federated optimization: Distributed optimization beyond the datacenter," arXiv preprint arXiv:1511.03575, 2015.
  59. C. Gentry, "Fully homomorphic encryption using ideal lattices," Proceedings of the Forty-first Annual ACM Symposium on Theory of Computing, pp.169-178, 2009.
  60. P. Li, J. Li, Z. Huang, T. Li, C.-Z Gao, S.-M. You, and K. Chen, "Multi-key privacy-preserving deep learning in cloud computing," Future Generation Computer Systems, Vol.74, pp.76-85, 2017. https://doi.org/10.1016/j.future.2017.02.006
  61. E. Bresson, D. Catalano, and D. Pointcheval, "A simple public-key cryptosystem with a double trapdoor decryption mechanism and its applications," International Conference on the Theory and Application of Cryptology and Information Security, Springer, Berlin, Heidelberg, pp.37-54, 2003.
  62. C. Gentry, "Fully homomorphic encryption using ideal lattices," Proceedings of the Forty-first Annual ACM Symposium on Theory of Computing, pp.169-178, 2009.
  63. T. Zhang, Z. He, and R. B. Lee, "Privacy-preserving machine learning through data obfuscation," arXiv preprint arXiv: 1807.01860, 2018.
  64. R. Shokri and V. Shmatikov, "Privacy-preserving deep learning," Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp.1310 -1321, 2015.
  65. M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, "Deep Learning with Differential Privacy," Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp.308-318, 2016.
  66. J. Wang, J. Zhang, W. Bao, X. Zhu, B. Cao, and P. S. Yu, "Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud," Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.2407-2416, 2018.
  67. Y. Mao, S. Yi, Q. Li, J. Feng, F. Xu, and S. Zhong, "Learning from differentially private neural activations with edge computing," 2018 IEEE/ACM Symposium on Edge Computing (SEC), pp.90-102, 2018.
  68. D. Li and Y. Liu, "Deep Learning in Natural Language Processing," Springer, 2018.
  69. Amazon [Internet], "Alexa Voice Service," 2016.
  70. Team Siri, "Hey Siri: An On-Device DNN-Powered Voice Trigger for Apple's Personal Assistant," Apple Machine Learning Journal, Vol.1, Issue 6, 2017
  71. M. S. Mehrjardi, A. Trablesi, and O. R. Zaiane, "Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems," arXiv preprint arXiv:1909.05246, 2019.
  72. S. P. Singh, A. Kumar, H. Darbari, L. Singh, A. Rastogi, and S. Jain, "Machine translation using deep learning: An overview," 2017 International Conference on Computer, Communications and Electronics (Comptelix), pp.162-167, 2017.
  73. X. Jiang, A. Hadid, Y. Pang, E. Granger, and X. Feng, "Deep Learning in Object Detection and Recognition," Springer:, 2019.
  74. V. Aggarwal and G. Kaur, "A review:deep learning technique for image classification," ACCENTS Transactions on Image Processing and Computer Vision, Vol.4, Issue 11, pp.21-25, 2018. https://doi.org/10.19101/TIPCV.2018.411003
  75. C. C. Hung, G. Ananthanarayanan, P. Bodik, L. Golubchik, M. Yu, V. Bahl, and M. Philipose, "VideoEdge: Processing Camera Streams using Hierarchical Clusters," 2018 IEEE/ACM Symposium on Edge Computing (SEC), IEEE, pp.115-131, 2018.
  76. S. Mittal, N. Negi, and R. Chauhan, "Integration of edge computing with cloud computing," 2017 International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT), pp.1-6, 2017.
  77. C. S. Lai, Y. Jia, Z. Dong, D. Wang, Y. Tao, Q. H. Lai, T. K. W. Richard, F. Z. Ahmed, R. Wu, and L. L Lai, "A Review of Technical Standards for Smart Cities," Clean Technologies, Vol.2, Issue 3, pp.290-310, 2020. https://doi.org/10.3390/cleantechnol2030019
  78. J. Souza, A. Francisco, C. Piekarski, and G. Prado, "Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018," Sustainability, Vol.11, Issue 4, pp.1077, 2019. https://doi.org/10.3390/su11041077
  79. F. Cicirelli, A. Guerrieri, G. Spezzano, and A. Vinci, "An edge-based platform for dynamic smart city applications," Future Generation Computer Systems, Vol.76, pp.106-118, 2017. https://doi.org/10.1016/j.future.2017.05.034
  80. A. Essien, I. Petrounias, P. Sampaio et al., "A deep-learning model for urban traffic flow prediction with traffic events mined from twitter," World Wide Web, pp.1-24, 2020
  81. Z. Liu, Z. Li, K. Wu, and M. Li., "Urban traffic prediction from mobility data using deep learning," IEEE Network, Vol.32, Issue 4, pp.40-46, 2018. https://doi.org/10.1109/MNET.2018.1700411
  82. Y. Liu, C. Yang, L. Jiang, S. Xie, and Y. Zhang, "Intelligent edge computing for IoT-based energy management in smart cities," IEEE Network, Vol.33, Issue 2, pp.111-117, 2019 https://doi.org/10.1109/mnet.2019.1800254
  83. L. Greco, G. Percannella, P. Ritrovato, and F. Tortorella, M. Vento, "Trends in IoT based solutions for health care: Moving AI to the edge," Pattern Recognition Letters, 2020.
  84. S. Tuli, N. Basumatary, S. S. Gill, M. Kahani, R. C. Arya, G. S.Wander, and R. Buyya, "Healthfog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heartdiseases in integrated iot and fog computing environments," Future Generation Computer Systems, Vol.104, pp.187-200, 2020 https://doi.org/10.1016/j.future.2019.10.043
  85. M. Chen, W. Li, Y. Hao, Y. Qian, and I. Humar, "Edge cognitive computing based smart healthcare system," Future Generation Computer Systems, Vol.86, pp.403-411, 2018. https://doi.org/10.1016/j.future.2018.03.054
  86. A. Esteva, B. Kuprel, R. Novoa, J. Ko, S. Swetter, H. Blau and S. Thrun, "Dermatologist-level classification of skin cancer with deep neural networks," nature, Vol.542, Issue 7639, pp.115-118, 2017. https://doi.org/10.1038/nature21056
  87. L. Tsochatzidis, L. Costaridou, and I. Pratikakis, "Deep Learning for Breast Cancer Diagnosis from Mammograms-A Comparative Study," Journal of Imaging, Vol.5, Issue 3, pp.37, 2019. https://doi.org/10.3390/jimaging5030037
  88. T. Ben-Nun and T. Hoefler, "Demystifying parallel and distributed deep learning: An in-depth concurrency analysis," ACM Computing Surveys (CSUR), Vol.52, Issue 4, 2018.
  89. R. Xu, J. B. Joshi, and C. Li, "CryptoNN: Training neural networks over encrypted data," 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, pp.1199-1209, 2019.