# Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution

arXiv: https://arxiv.org/abs/2011.02883 · HTML: https://mlsanity.com/paper/2011.02883/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Flexible Clustered Federated Learning for Client-Level Data Distribution Shift](https://mlsanity.com/paper/2108.09749/) (2021)
- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [FedSyn: Synthetic Data Generation using Federated Learning](https://mlsanity.com/paper/2203.05931/) (2022)
- [Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions](https://mlsanity.com/paper/2110.04160/) (2021)

## Abstract (arXiv)

In this work, we propose a collaborative city digital twin based on FL, a novel paradigm that allowing multiple city DT to share the local strategy and status in a timely manner. In particular, an FL central server manages the local updates of multiple collaborators (city DT), provides a global model which is trained in multiple iterations at different city DT systems, until the model gains the correlations between various response plan and infection trend. That means, a collaborative city DT paradigm based on FL techniques can obtain knowledge and patterns from multiple DTs, and eventually establish a `global view' for city crisis management. Meanwhile, it also helps to improve each city digital twin selves by consolidating other DT's respective data without violating privacy rules. To validate the proposed solution, we take COVID-19 pandemic as a case study. The experimental results on the real dataset with various response plan validate our proposed solution and demonstrate the superior performance.