Summary of Federated Foundation Models on Heterogeneous Time Series, by Shengchao Chen et al.
Federated Foundation Models on Heterogeneous Time Series
by Shengchao Chen, Guodong Long, Jing Jiang, Chengqi Zhang
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 federated learning approach, called FFTS, is proposed to train time series foundation models that generalize well across diverse applications from scratch. The existing cross-domain fusion method is ineffective due to statistical heterogeneity across domains. To address this challenge, the proposed approach treats each data-holding organization as an independent client in a collaborative framework and trains local models to preserve unique characteristics per dataset. A new regularization mechanism is applied at both the client and server levels to align shared knowledge across heterogeneous datasets. The method achieves superior generalization capabilities on benchmark datasets for tasks such as forecasting, imputation, and anomaly detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a way to train time series models that work well with different types of data from many sources. Right now, it’s hard to share knowledge between these different sources because they are very different. To fix this, the authors suggest a new method called FFTS. This method trains separate models for each source of data and then combines them in a way that preserves what makes each one unique. The result is better models that can make accurate predictions, fill in missing values, and detect unusual patterns. |
Keywords
» Artificial intelligence » Anomaly detection » Federated learning » Generalization » Regularization » Time series
