{"type":"ScholarlyArticle","title":"Federated Prompt Learning for Weather Foundation Models on Devices","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"},{"name":"Chengqi Zhang","slug":"chengqi-zhang","given":"Chengqi","family":"Zhang","affiliation":"Australian Artificial Intelligence Institute , FEIT , University of Technology Sydney"}],"abstract":"On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing, holds significance for supporting human activates. Federated Learning is a promising solution for such forecasting by enabling collaborative model training without sharing raw data. However, it faces three main challenges that hinder its reliability: (1) data heterogeneity among devices due to geographic differences; (2) data homogeneity within individual devices and (3) communication overload from sending large model parameters for collaboration. To address these challenges, this paper propose Federated Prompt learning for Weather Foundation Models on Devices (FedPoD), which enables devices to obtain highly customized models while maintaining communication efficiency. Concretely, our Adaptive Prompt Tuning leverages lightweight prompts guide frozen foundation model to generate more precise predictions, also conducts prompt-based multi-level communication to encourage multi-source knowledge fusion and regulate optimization. Additionally, Dynamic Graph Modeling constructs graphs from prompts, prioritizing collaborative training among devices with similar data distributions to against heterogeneity. Extensive experiments demonstrates FedPoD leads the performance among state-of-the-art baselines across various setting in real-world on-device weather forecasting datasets.","doi":"10.24963/ijcai.2024/638","url":"https://ijcai.org/proceedings/2024/638","pdf":"https://ijcai.org/proceedings/2024/0638.pdf","markdown":"https://ijcai.org/proceedings/2024/638.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/638","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":638,"pages":{"first":5772,"last":5780},"keywords":["Machine Learning: ML: Applications","Machine Learning: ML: Federated learning","Data Mining: DM: Mining heterogenous data","Multidisciplinary Topics and Applications: MTA: Energy, environment and sustainability"],"videos":[],"references":[{"position":1,"raw":"[Chakraborty and Rodrigues, 2020] Chinmay Chakraborty and Joel JCP Rodrigues. 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