Summary of Corast: Towards Foundation Model-powered Correlated Data Analysis in Resource-constrained Cps and Iot, by Yi Hu et al.
CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT
by Yi Hu, Jinhang Zuo, Alanis Zhao, Bob Iannucci, Carlee Joe-Wong
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Foundation models (FMs) have emerged as a promising solution for harnessing distributed and diverse environmental data, leveraging prior knowledge to understand complex temporal and spatial correlations within heterogeneous datasets. Unlike federated learning frameworks, which often struggle with multimodal data, FMs can transform diverse inputs into embeddings, facilitating the integration of information from various modalities and application of prior learning to new domains. However, deploying FMs in resource-constrained edge systems poses significant challenges. To address this, we introduce CoRAST, a novel learning framework that utilizes FMs for enhanced analysis of distributed, correlated heterogeneous data. CoRAST leverages a server-based FM to extract temporal, spatial, and cross-modal correlations among sensor data, enabling context-aware insights for localized client tasks through FM-powered global representation learning. Our evaluation on real-world weather datasets demonstrates CoRAST’s ability to reduce forecast errors by up to 50.3% compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a new kind of artificial intelligence model called a foundation model (FM) to analyze big amounts of data from different sources, like weather stations or cameras. FMs can combine information from different types of data and apply what they’ve learned to new situations. However, these models are usually too big for small devices like smartphones to handle. The authors created a new system called CoRAST that uses an FM in the cloud to help devices make better predictions about things like weather forecasts. They tested CoRAST on real-world weather data and found it was up to 50% more accurate than other methods. |
Keywords
* Artificial intelligence * Federated learning * Representation learning
