Prompt Federated Learning for Weather Forecasting: Toward Foundation
Models on Meteorological Data
- URL: http://arxiv.org/abs/2301.09152v2
- Date: Sat, 27 May 2023 09:11:48 GMT
- Title: Prompt Federated Learning for Weather Forecasting: Toward Foundation
Models on Meteorological Data
- Authors: Shengchao Chen, Guodong Long, Tao Shen, Jing Jiang
- Abstract summary: To tackle the global climate challenge, it urgently needs to develop a collaborative platform for comprehensive weather forecasting on large-scale meteorological data.
This paper develops a foundation model across regions of understanding complex meteorological data and providing weather forecasting.
A novel prompt learning mechanism has been adopted to satisfy low-resourced sensors' communication and computational constraints.
- Score: 37.549578998407675
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To tackle the global climate challenge, it urgently needs to develop a
collaborative platform for comprehensive weather forecasting on large-scale
meteorological data. Despite urgency, heterogeneous meteorological sensors
across countries and regions, inevitably causing multivariate heterogeneity and
data exposure, become the main barrier. This paper develops a foundation model
across regions capable of understanding complex meteorological data and
providing weather forecasting. To relieve the data exposure concern across
regions, a novel federated learning approach has been proposed to
collaboratively learn a brand-new spatio-temporal Transformer-based foundation
model across participants with heterogeneous meteorological data. Moreover, a
novel prompt learning mechanism has been adopted to satisfy low-resourced
sensors' communication and computational constraints. The effectiveness of the
proposed method has been demonstrated on classical weather forecasting tasks
using three meteorological datasets with multivariate time series.
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