  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.
