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On-device intelligence for weather forecasting uses local deep learningmodels to analyze weather patterns without centralized cloud computing, holdssignificance for supporting human activates. Federated Learning is a promisingsolution for such forecasting by enabling collaborative model training withoutsharing raw data. However, it faces three main challenges that hinder itsreliability: (1) data heterogeneity among devices due to geographicdifferences; (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 forWeather Foundation Models on Devices (FedPoD), which enables devices to obtainhighly customized models while maintaining communication efficiency.Concretely, our Adaptive Prompt Tuning leverages lightweight prompts guidefrozen foundation model to generate more precise predictions, also conductsprompt-based multi-level communication to encourage multi-source knowledgefusion and regulate optimization. Additionally, Dynamic Graph Modelingconstructs graphs from prompts, prioritizing collaborative training amongdevices with similar data distributions to against heterogeneity. Extensiveexperiments demonstrates FedPoD leads the performance among state-of-the-artbaselines across various setting in real-world on-device weather forecastingdatasets.

Federated Prompt Learning for Weather Foundation Models on Devices