Cited by 186Privacy-Preserving Technologies in DataSmart Cities and TechnologiesCOVID-19 Digital Contact Tracing

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Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

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Pang, Junjie, Huang, Yan, Xie, Zhenzhen, Li, Jianbo, Cai, Zhipeng·2021·Tsinghua Science and Technology

DOI 10.26599/tst.2021.9010026

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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.

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Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018) · arXiv · doi:10.48550/arxiv.1811.03604 · Cited by 1,148
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Chetoui, M., & Akhloufi, M. A. (2023) · Computers · doi:10.3390/computers12050106 · Cited by 15
A spatiotemporal intelligent framework and experimental platform for urban digital twins
Hu, J., Lv, Z., Yuan, D., He, B., Chen, W., Ye, X., Li, D., & Yang, G. (2023) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.08.018 · Cited by 16
Deep Reinforcement Learning Based Data-Driven Mapping Mechanism of Digital Twin for Internet of Energy
Xu, S., Guan, X., Peng, Y., Liu, Y., Cui, C., Chen, H., Ohtsuki, T. O., & Han, Z. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3390797 · Cited by 15
Artificial Intelligence-Aided Digital Twin Design: A Systematic ReviewPreprint
Hao, N., Li, Y., Liu, K., Liu, S., Lu, Y., Xu, B., Li, C., Chen, J., Yue, L., Fu, T., et al. (2024) · Preprints.org · doi:10.20944/preprints202408.2063.v1 · Cited by 16
Federated learning‐based private medical knowledge graph for epidemic surveillance in internet of things
Wu, X., Gao, J., Bilal, M., Dai, F., Xu, X., Qi, L., & Dou, W. (2023) · Expert Systems · doi:10.1111/exsy.13372 · Cited by 14
LTKT: Knowledge Tracing Based on Positive and Negative Learning Transfers
Xu, J. W., Tang, R., Lv, P., Yu, M., Yu, G., & Chen, E. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010201 · Cited by 12
Intra-Patient and Inter-Patient Multi-Classification of Severe Cardiovascular Diseases Based on CResFormer
Li, D., Shi, C., Zhao, J., Liu, Y., & Li, C. (2022) · Tsinghua Science & Technology · doi:10.26599/tst.2022.9010008 · Cited by 12
Digital Twin Collaborative Platforms: Applications to Humans-in-the-Loop Crafting of Urban Areas
Bononi, L., Donatiello, L., Longo, D., Massari, M., Montori, F., Stacchio, L., & Marfia, G. (2022) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2022.3214944 · Cited by 12
Survey, taxonomy, and emerging paradigms of societal digital twins for public health preparedness
Rehan, W., & Rehan, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01737-5 · Cited by 11
Decentralized Aggregation Design and Study of Federated Learning
Malladi, V. S., Li, Y. J., Siddula, M., Seoand, D., & Huang, Y. (2021) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/swc50871.2021.00052 · Cited by 11
Self-citation
State of the Art of Urban Digital Twin Platforms
Martella, A., Ramadan, A. I. H. A., Martella, C., Patano, M., & Longo, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43401-3_20 · Cited by 11
A research on digital learning system based on multiplayer online animation game
Liu, L., Li, M., & Ji, S. (2022) · Library Hi Tech · doi:10.1108/lht-10-2021-0354 · Cited by 11
Non-IID Medical Imaging Data on COVID-19 in the Federated Learning Framework: Impact and Directions
Al-Hafiz, F., & Basuhail, A. A. (2024) · COVID · doi:10.3390/covid4120140 · Cited by 11
Design of Chinese Opera Cultural Platform Based on Digital Twins and Research on International Cultural Communication Strategies
Ma, L. (2022) · Mobile Information Systems · doi:10.1155/2022/6996377 · Cited by 10
Retracted
Towards a Digital Twin of Society
Chircu, A. M., Czarnecki, C., Friedmann, D., Pomaskow, J., & Sultanow, E. G. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.816 · Cited by 9
Federated data acquisition market: Architecture and a mean-field based data pricing strategy
Hu, J., Reed, M. J., Zhang, K., Liu, Z., & Hu, J. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100232 · Cited by 10
Advancing Intra and Inter-City Urban Digital Twins: An Updated Review
Wu, T., & Guan, C. (2024) · Journal of Planning Education and Research · doi:10.1177/0739456x241260887 · Cited by 11
Heterogeneous intensity-based DBSCAN (iDBSCAN) model for urban attention distribution in digital twin cities
Jiang, Y., Liu, Q., Zhao, S., Zhang, T., Fan, X., Zhong, R. Y., & Huang, G. Q. (2024) · Digital engineering. · doi:10.1016/j.dte.2024.100014 · Cited by 10
Analysis of urban management information technology systems: Ubiquitous City, Smart City, Metaverse, and Digital Twin
Kim, J.-H., & Kim, M. J. (2025) · International Journal of Urban Sciences · doi:10.1080/12265934.2025.2452501 · Cited by 11

Authors: Pang, J., Huang, Y., Xie, Z., Li, J., & Cai, Z.
Affiliations: Qingdao University (CN)
Kennesaw State University (US)
Jilin University (CN)
Georgia State University (US)
ORCID: Pang, Junjie
Huang, Yan
Li, Jianbo
Cai, Zhipeng
Venue: Tsinghua Science and Technology
Publisher: Tsinghua University Press
Published: 2021-10
Volume: 26
Issue: 5
Pages: 759-771
ISSN: 1007-0214 (ISSN-L)
DOI: 10.26599/tst.2021.9010026
OpenAlex: W3158590963
Cited by: 186
References: 35
Citations by year: 2026: 4 · 2025: 26 · 2024: 34 · 2023: 45 · 2022: 54 · 2021: 23
FWCI: 13.79
Type: journal article
Language: en
Open access: diamond
doi.org
Licence: cc-by
Locations: Tsinghua Science & Technology (publishedVersion, cc-by)
DigitalCommons - Kennesaw State University (Kennesaw State University) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders: National Natural Science Foundation of China (No. 61701190, 61701190, 2017YFA0604500)
National Key Research and Development Program of China (2018YFB2100303, 2017YFA0604500, 61701190)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Social Sciences › Urban Studies › Smart Cities and Technologies
Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
Keywords: digital twin cities, federated learning, COVID-19 pandemic, cooperative learning, crisis management, privacy-preserving data sharing, pandemic response planning

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Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

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