Federated Prompt Learning for Weather Foundation Models on Devices

arXiv:2305.14244v2 Announce Type: replace
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 …

abstract analyze arxiv cloud cloud computing collaborative computing cs.lg data deep learning devices enabling federated learning forecasting foundation however human intelligence patterns prompt prompt learning raw significance solution training type weather weather forecasting