论文标题
COVID-19大流行的合作城市数字双胞胎：联邦学习解决方案
Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution
论文作者
论文摘要
在这项工作中，我们提出了一个基于FL的协作城市数字双胞胎，这是一种新颖的范式，允许多个城市DT及时分享本地战略和地位。特别是，FL Central Server管理了多个合作者（City DT）的本地更新，提供了一个全球模型，该模型在不同的City DT系统的多次迭代中进行了培训，直到该模型获得了各种响应计划和感染趋势之间的相关性。这意味着，基于FL技术的协作城市DT范式可以从多个DT中获取知识和模式，并最终为城市危机管理建立“全球观点”。同时，它还通过在不违反隐私规则的情况下巩固其他DT的数据来改善每个城市数字双胞胎自我。为了验证提出的解决方案，我们将Covid-19-19作为案例研究。具有各种响应计划的真实数据集上的实验结果验证了我们提出的解决方案，并证明了卓越的性能。
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.
