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Open Access

Collaborative City Digital Twin for the COVID-19 Pandemic: A Federated Learning Solution

College of Computer Science and Technology, Qingdao University, Qingdao 266000, China
Business School, Qingdao University, Qingdao 266000, China
College of Computing and Software Engineering, Kennesaw State University, Atlanta, GA 30060, USA
College of Computer Science and Technology, Jilin University, Changchun 130012, China
College of Computer Science and Technology, Qingdao University, Qingdao 266000, China
Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
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Abstract

The novel coronavirus, COVID-19, has caused a crisis that affects all segments of the population. As the knowledge and understanding of COVID-19 evolve, an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the virus. Recent studies indicate that a city Digital Twin (DT) is beneficial for tackling this health crisis, because it can construct a virtual replica to simulate factors, such as climate conditions, response policies, and people’s trajectories, to help plan efficient and inclusive decisions. However, a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions, limiting its advantages when facing urgent crises, such as the COVID-19 pandemic. Federated Learning (FL), in which all clients can learn a shared model while retaining all training data locally, emerges as a promising solution for accumulating the insights from multiple data sources efficiently. Furthermore, the enhanced privacy protection settings removing the privacy barriers lie in this collaboration. In this work, we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status quickly. In particular, an FL central server manages the local updates of multiple collaborators (city DTs), providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection trends. This approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a "global view" of city crisis management. Meanwhile, it also helps improve each city’s DT by consolidating other DT’s data without violating privacy rules. In this paper, we use the COVID-19 pandemic as the use case of the proposed framework. The experimental results on a real dataset with various response plans validate our proposed solution and demonstrate its superior performance.

References

[1]
WHO, Coronavirus disease (COVID-19) pandemic, https://www.who.int/emergencies/diseases/novel-coronavirus-2019, 2020.
[2]
S. Kumar and M. Singh, Big data analytics for healthcare industry: Impact, applications, and tools, Big Data Mining and Analytics, vol. 2, no. 1, pp. 48–57, 2019.
[3]
W. Zhong, N. Yu, and C. Y. Ai, Applying big data based deep learning system to intrusion detection, Big Data Mining and Analytics, vol. 3, no. 3, pp. 181–195, 2020.
[4]
H. Nieto-Chaupis, Face to face with next flu pandemic with a wiener-series-based machine learning: Fast decisions to tackle rapid spread, in Proc. 2019 IEEE 9th Annu. Computing and Communication Workshop and Conf., Las Vegas, NV, USA, 2019, pp. 654–658.
[5]
B. Adhikari, X. F. Xu, N. Ramakrishnan, and B. A. Prakash, EpiDeep: Exploiting embeddings for epidemic forecasting, in Proc. 25th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, Anchorage, AK, USA, 2019, pp. 577–586.
[6]
Z. B. He, Y. S. Li, J. Li, K. Y. Li, Q. Cai, and Y. Liang, Achieving differential privacy of genomic data releasing via belief propagation, Tsinghua Science and Technology, vol. 23, no. 4, pp. 389–395, 2018.
[7]
X. Zheng, Z. P. Cai, and Y. S. Li, Data linkage in smart internet of things systems: A consideration from a privacy perspective, IEEE Communications Magazine, vol. 56, no. 9, pp. 55–61, 2018.
[8]
Z. P. Cai, Z. B. He, X. Guan, and Y. S. Li, Collective data-sanitization for preventing sensitive information inference attacks in social networks, IEEE Transactions on Dependable and Secure Computing, vol. 15, no. 4, pp. 577–590, 2018.
[9]
A. Croatti, M. Gabellini, S. Montagna, and A. Ricci, On the integration of agents and digital twins in healthcare, J. Med. Syst., vol. 44, no. 9, p. 161, 2020.
[10]
N. Bagaria, F. Laamarti, H. F. Badawi, A. Albraikan, R. A. M. Velazquez, and A. El-Saddik, Health 4.0: Digital twins for health and well-being, in Connected Health in Smart Cities, A. El-Saddik, M. S. Hossain, and B. Kantarci, eds. Switzerland: Springer, 2020, pp. 143–152.
[11]
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, Communication-efficient learning of deep networks from decentralized data, in Proc. 20th Int. Conf. Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 2017, pp. 1273–1282.
[12]
J. J. Pang, Y. Huang, Z. Z. Xie, Q. L. Han, and Z. P. Cai, Realizing the heterogeneity: A self-organized federated learning framework for IoT, IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3088–3098, 2021.
[13]
J. J. Li, H. H. Jiao, J. Wang, Z. G. Liu, and J. Wu, Online real-time trajectory analysis based on adaptive time interval clustering algorithm, Big Data Mining and Analytics, vol. 3, no. 2, pp. 131–142, 2020.
[14]
K. Yang, J. H. Zhu, and X. Guo, POI neural-rec model via graph embedding representation, Tsinghua Science and Technology, vol. 26, no. 2, pp. 208–218, 2021.
[15]
Z. Wang, C. K. Wang, X. J. Ye, J. S. Pei, and B. Li, Propagation history ranking in social networks: A causality-based approach, Tsinghua Science and Technology, vol. 25, no. 2, pp. 161–179, 2020.
[16]
H. X. Chen, S. Feng, X. Pei, Z. Zhang, and D. Y. Yao, Dangerous driving behavior recognition and prevention using an autoregressive time-series model, Tsinghua Science and Technology, vol. 22, no. 6, pp. 682–690, 2017.
[17]
Z. L. Ye, H. X. Zhao, K. Zhang, Z. Y. Wang, and Y. Zhu, Network representation based on the joint learning of three feature views, Big Data Mining and Analytics, vol. 2, no. 4, pp. 248–260, 2019.
[18]
L. J. Wang, J. Z. Chen, and M. Marathe, DEFSI: Deep learning based epidemic forecasting with synthetic information, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 9607–9612, 2019.
[19]
L. Zhao, J. Z. Chen, F. Chen, W. Wang, C. T. Lu, and N. Ramakrishnan, SimNest: Social media nested epidemic simulation via online semi-supervised deep learning, in Proc. 2015 IEEE Int. Conf. Data Mining, Atlantic City, NJ, USA, 2015, pp. 639–648.
[20]
B. Y. Shi, J. N. Zhong, Q. Bao, H. J. Qiu, and J. M. Liu, EpiRep: Learning node representations through epidemic dynamics on networks, in Proc. 2019 IEEE/WIC/ACM Int. Conf. Web Intelligence, Thessaloniki, Greece, 2019, pp. 486–492.
[21]
Y. Z. Zhou, D. Zhang, and N. X. Xiong, Post-cloud computing paradigms: a survey and comparison, Tsinghua Science and Technology, vol. 22, no. 6, pp. 714–732, 2017.
[22]
H. Yang, F. Li, D. X. Yu, Y. F. Zou, and J. G. Yu, Reliable data storage in heterogeneous wireless sensor networks by jointly optimizing routing and storage node deployment, Tsinghua Science and Technology, vol. 26, no. 2, pp. 230–238, 2021.
[23]
F. S. Lu, M. W. Hattab, C. L. Clemente, M. Biggerstaff, and M. Santillana, Improved state-level influenza nowcasting in the United States leveraging internet-based data and network approaches, Nature Communications, vol. 10, p. 147, 2019.
[24]
B. Zou, V. Lampos, and I. Cox, Multi-task learning improves disease models from web search, in Proc. 2018 World Wide Web Conf., Lyon, France, 2018, pp. 87–96.
[25]
Z. P. Cai and X. Zheng, A private and efficient mechanism for data uploading in smart cyber-physical systems, IEEE Trans. Netw. Sci. Eng., vol. 7, no. 2, pp. 766–775, 2020.
[26]
Z. P. Cai, X. Zheng, and J. G. Yu, A differential-private framework for urban traffic flows estimation via taxi companies, IEEE Transactions on Industrial Informatics, vol. 15, no. 12, pp. 6492–6499, 2019.
[27]
C. Fan, Y. C. Jiang, and A. Mostafavi, Social sensing in disaster city digital twin: Integrated textual–visual–geo framework for situational awareness during built environment disruptions, Journal of Management in Engineering, vol. 36, no. 3, p. 04020002, 2020.
[28]
C. Fan, C. Zhang, A. Yahja, and A. Mostafavi, Disaster city digital twin: A vision for integrating artificial and human intelligence for disaster management, International Journal of Information Management, vol. 56, p. 102049, 2021.
[29]
A. Francisco, N. Mohammadi, and J. E. Taylor, Smart city digital twin–enabled energy management: Toward real-time urban building energy benchmarking, Journal of Management in Engineering, vol. 36, no. 2, p. 04019045, 2020.
[30]
F. Xue, W. S. Lu, Z. Chen, and C. J. Webster, From LiDAR point cloud towards digital twin city: Clustering city objects based on gestalt principles, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 167, pp. 418–431, 2020.
[31]
W. J. Holstein, Virtual Singapore-creating an intelligent 3D model to improve experiences of residents, business and government, http://www.3dexperiencecity.com/, 2016.
[32]
F. Dembski, U. Wössner, M. Letzgus, M. Ruddat, and C. Yamu, Urban digital twins for smart cities and citizens: The case study of Herrenberg, Germany, Sustainability, vol. 12, no. 6, p. 2307, 2020.
[33]
Z. P. Cai and Z. B. He, Trading private range counting over big IoT data, in Proc. 39th IEEE Int. Conf. Distributed Computing Systems, Dallas, TX, USA, 2019, pp. 144–153.
[34]
X. Zheng and Z. P. Cai, Privacy-preserved data sharing towards multiple parties in industrial IoTs, IEEE J. Sel. Areas Commun., vol. 38, no. 5, pp. 968–979, 2020.
[35]
A. Hard, K. Rao, R. Mathews, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage, Federated learning for mobile keyboard prediction, arXiv preprint arXiv: 1811.03604, 2018.
[36]
Y. Q. Chen, X. Qin, J. D. Wang, C. H. Yu, and W. Gao, FedHealth: A federated transfer learning framework for wearable healthcare, IEEE Intell. Syst., vol. 35, no. 4, pp. 83–93, 2020.
[37]
G. D. Long, Y. Tan, J. Jiang, and C. Q. Zhang, Federated learning for open banking, in Federated Learning. Lecture Notes in Computer Science, vol 12500, Q. Yang, L. X. Fan, and H. Yu, eds. Switzerland: Springer, 2020, pp. 240–254.
[38]
S. J. Bai, J. Z. Kolter, and V. Koltun, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv preprint arXiv: 1803.01271, 2018.
[39]
N. Mohammadi and J. E. Taylor, Smart city digital twins, in Proc. 2017 IEEE Symp. Series on Computational Intelligence, Honolulu, HI, USA, 2017, pp. 1–5.
[40]
T. Ryffel, A. Trask, M. Dahl, B. Wagner, J. Mancuso, D. Rueckert, and J. Passerat-Palmbach, A generic framework for privacy preserving deep learning, arXiv preprint arXiv: 1811.04017, 2018.
Tsinghua Science and Technology
Pages 759-771
Cite this article:
Pang J, Huang Y, Xie Z, et al. Collaborative City Digital Twin for the COVID-19 Pandemic: A Federated Learning Solution. Tsinghua Science and Technology, 2021, 26(5): 759-771. https://doi.org/10.26599/TST.2021.9010026

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Received: 26 February 2021
Accepted: 18 March 2021
Published: 20 April 2021
© The author(s) 2021

© The author(s) 2021. The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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