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.

翻译：联邦气象预报是一种有前景的协作学习框架，用于分析来自不同国家和地区参与者的气象数据，从而构建一个全球规模的实时天气数据预测分析平台以应对气候变化。本文旨在对联邦场景下的气象数据进行建模，其中大量分布的低资源传感器部署在不同位置。具体而言，我们将时空气象数据建模到联邦提示学习框架中，利用轻量级提示在参与者间共享有意义的表示和结构知识。基于提示的通信允许服务器建立参与者之间的结构拓扑关系，并进一步探索复杂的时空相关性，而无需传输私有数据，同时减轻通信开销。此外，除了服务器上的全局共享大模型外，我们的方法使每个参与者能够获得一个高度定制化的个性化模型，以应对特定地理区域的气候变化。我们通过利用三个时空多变量时间序列气象数据，在经典气象预报任务上验证了该方法的有效性。