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

Yi Hu, Jinhang Zuo, Alanis Zhao, Bob Iannucci, Carlee Joe-Wong

TL;DR

CoRAST is introduced, a novel learning framework that utilizes FMs for enhanced analysis of distributed, correlated heterogeneous data through environmental representation learning and enables CoRAST to offer context-aware insights for localized client tasks through FM-powered global representation learning.

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 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.

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

TL;DR

CoRAST is introduced, a novel learning framework that utilizes FMs for enhanced analysis of distributed, correlated heterogeneous data through environmental representation learning and enables CoRAST to offer context-aware insights for localized client tasks through FM-powered global representation learning.

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 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.
Paper Structure (13 sections, 3 figures, 4 tables)

This paper contains 13 sections, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. CoRAST
  4. Server-Based Representation Learning
  5. Graph Neural Networks (GNNs)
  6. Attention Mechanism
  7. Client Local Training
  8. Continual Learning
  9. Resource Usage
  10. Experiments
  11. Aligned Objective - H2CO Forecast
  12. Multimodal Tasks - Local Forecast
  13. Conclusion and Future Work

Figures (3)

  • Figure 1: Correlated Data in CPS and IoT
  • Figure 2: CoRAST framework. ① Global Representation Learning -- A server-based FM is pre-trained and fine-tuned on historical environment data for global representation learning in a self-supervised manner. ② Representation Distribution -- Using the environment data that correlate with client local data, the FM produces and distributes contextual representations to edge clients aiding downstream local tasks. ③ Local Learning with Global Context -- Clients integrate global contexts with their local data for independent local learning, leveraging broad environmental insights with local datasets.
  • Figure 3: MSE Train Loss with the standard deviation represented by a shaded area around the loss curves. CoRAST has the lowest training loss in all settings when clients share the same local training task or have distinct local training tasks.