Spatial-temporal Prompt Learning for Federated Weather Forecasting
Shengchao Chen Guodong Long Tao Shen Tianyi Zhou Jing Jiang
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.