Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution
This work addresses city crisis management during pandemics by enabling collaborative decision-making, though it is incremental as it applies existing federated learning to a new domain.
The authors tackled the problem of coordinating city-level pandemic response by proposing a federated learning-based digital twin system that shares strategies across cities without violating privacy, achieving superior performance on real COVID-19 data.
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.