Summary of A Two-stage Federated Learning Approach For Industrial Prognostics Using Large-scale High-dimensional Signals, by Yuqi Su et al.
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals
by Yuqi Su, Xiaolei Fang
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP)
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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 A novel statistical learning-based approach is introduced for industrial prognostics, enabling multiple organizations to jointly train a predictive model while keeping their high-dimensional degradation signals private. The federated model consists of two stages: federated dimension reduction via randomized singular value decomposition and federated log-location-scale regression. This framework overcomes the limitations of existing methods by leveraging statistical learning techniques that perform well with smaller datasets, providing comprehensive failure time distributions. Evaluation is conducted using simulated data and a NASA repository dataset, demonstrating the effectiveness and practicality of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Industrial prognostics aims to predict asset failures using high-dimensional degradation signals. But organizations often lack sufficient data for reliable predictions, and sharing data between organizations can raise privacy concerns. To solve this problem, researchers propose a new model that allows multiple organizations to work together while keeping their data private. The model consists of two parts: reducing the dimensionality of the data and estimating failure times. This approach uses statistical techniques that work well with smaller datasets and provides detailed predictions of when failures will occur. |
Keywords
» Artificial intelligence » Regression
