The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index.
Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution
Open accessAbstract
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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“Pang et al. [91] created a federated model based on a digital city twin concept to predict outcomes of different COVID-19 prevention plans over time and assess the survival rates of multiple cities over the study period.”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [91]
In the citing paper
“Furthermore, using the digital city twin platform, they were able to track the effectiveness of each prevention plan and create local models which were sent to the federated sites for safety.”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [91]
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Digital twin: origin to future
Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N., & Devine, D. (2021) · Applied System Innovation · doi:10.3390/asi4020036 · Cited by 1,038
Digital Twin Technology Challenges and Applications: A Comprehensive Review
Botín-Sanabria, D. M., Mihaita, A.-S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., & Lozoya-Santos, J. D. J. (2022) · Remote Sensing · doi:10.3390/rs14061335 · Cited by 878
Fusing Blockchain and AI With Metaverse: A Survey
Yang, Q., Zhao, Y., Huang, H., Xiong, Z., Kang, J., & Zheng, Z. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3188249 · Cited by 576
Wang, Y., Su, Z., Guo, S., Dai, M., Luan, T. H., & Liu, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3263909 · Cited by 357
Digital Twins From Smart Manufacturing to Smart Cities: A Survey
Mylonas, G., Kalogeras, A. P., Kalogeras, G., Anagnostopoulos, C., Alexakos, C., & Muñoz, L. (2021) · IEEE Access · doi:10.1109/access.2021.3120843 · Cited by 327
Metadata
- 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)
- Venue
- Tsinghua Science and Technology
- Publisher
- Tsinghua University Press
- Published
- 2021-10
- Volume
- 26
- Issue
- 5
- Pages
- 759-771
- ISSN
- 1007-0214 (ISSN-L)
- 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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