  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.
