{"type":"ScholarlyArticle","title":"Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data","authors":[{"name":"Shengchao Chen","slug":"shengchao-chen","given":"Shengchao","family":"Chen","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney"},{"name":"Guodong Long","slug":"guodong-long","given":"Guodong","family":"Long","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney"},{"name":"Tao Shen","slug":"tao-shen","given":"Tao","family":"Shen","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney"},{"name":"Jing Jiang","slug":"jing-jiang","given":"Jing","family":"Jiang","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney"}],"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.","doi":"10.24963/ijcai.2023/393","url":"https://ijcai.org/proceedings/2023/393","pdf":"https://ijcai.org/proceedings/2023/0393.pdf","markdown":"https://ijcai.org/proceedings/2023/393.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/393","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":393,"pages":{"first":3532,"last":3540},"keywords":["Machine Learning: ML: Time series and data streams","Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Alléon et al., 2020] Antoine Alléon, Grégoire Jauvion, Boris Quennehen, and David Lissmyr. 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