CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT

arXiv:2403.18451v1 Announce Type: new
Abstract: 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 …

abstract analysis arxiv correlations cs.ai cs.lg data data analysis datasets distributed distributed learning diverse environmental environmental data federated learning foundation foundation model frameworks harness iot knowledge multimodal prior solution spatial struggle temporal type