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To tackle the global climate challenge, it urgently needs to develop acollaborative platform for comprehensive weather forecasting on large-scalemeteorological data. Despite urgency, heterogeneous meteorological sensorsacross countries and regions, inevitably causing multivariate heterogeneity anddata exposure, become the main barrier. This paper develops a foundation modelacross regions capable of understanding complex meteorological data andproviding weather forecasting. To relieve the data exposure concern acrossregions, a novel federated learning approach has been proposed tocollaboratively learn a brand-new spatio-temporal Transformer-based foundationmodel across participants with heterogeneous meteorological data. Moreover, anovel prompt learning mechanism has been adopted to satisfy low-resourcedsensors' communication and computational constraints. The effectiveness of theproposed method has been demonstrated on classical weather forecasting tasksusing three meteorological datasets with multivariate time series.

Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data