  Foundation models (FMs) emerge as a promising solution to harness distributed
and diverse environmental data by leveraging prior knowledge to understand the
complicated temporal and spatial correlations within heterogeneous datasets.
Unlike distributed learning frameworks such as federated learning, which often
struggle with multimodal data, FMs can transform diverse inputs into
embeddings. This process facilitates the integration of information from
various modalities and the application of prior learning to new domains.
However, deploying FMs in resource-constrained edge systems poses significant
challenges. To this end, we introduce CoRAST, a novel learning framework that
utilizes FMs for enhanced analysis of distributed, correlated heterogeneous
data. Utilizing a server-based FM, CoRAST can exploit existing environment
information to extract temporal, spatial, and cross-modal correlations among
sensor data. This enables CoRAST to offer context-aware insights for localized
client tasks through FM-powered global representation learning. Our evaluation
on real-world weather dataset demonstrates CoRAST's ability to exploit
correlated heterogeneous data through environmental representation learning to
reduce the forecast errors by up to 50.3% compared to the baselines.
