Federated weather forecasting is a promising collaborative learning framework
for analyzing meteorological data across participants from different countries
and regions, thus embodying a global-scale real-time weather data predictive
analytics platform to tackle climate change. This paper is to model the
meteorological data in a federated setting where many distributed low-resourced
sensors are deployed in different locations. Specifically, we model the
spatial-temporal weather data into a federated prompt learning framework that
leverages lightweight prompts to share meaningful representation and structural
knowledge among participants. Prompts-based communication allows the server to
establish the structural topology relationships among participants and further
explore the complex spatial-temporal correlations without transmitting private
data while mitigating communication overhead. Moreover, in addition to a
globally shared large model at the server, our proposed method enables each
participant to acquire a personalized model that is highly customized to tackle
climate changes in a specific geographic area. We have demonstrated the
effectiveness of our method on classical weather forecasting tasks by utilizing
three spatial-temporal multivariate time-series weather data.

通过搭建一个基于联邦学习的时空气象数据预测分析框架，本研究在一个去中心化的环境下建立了一个空间 - 时间的气象数据模型，实现了低成本、高效率、安全共享的监督学习和知识融合。在共享大型的融合模型同时，每个参与者还可根据不同的地理位置获取个性化的定制模型来应对气候变化。实验结果表明，该方法可以有效地优化经典气象预测任务。