Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| _version_ | 1866917625447055360 |
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| author | Yue, Xubo Kontar, Raed Al |
| author_facet | Yue, Xubo Kontar, Raed Al |
| contents | In this paper, we propose \texttt{FGPR}: a Federated Gaussian process ($\mathcal{GP}$) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local client computations. Notably, the resulting global model excels in personalization as \texttt{FGPR} jointly learns a global $\mathcal{GP}$ prior across all clients. The predictive posterior then is obtained by exploiting this prior and conditioning on local data which encodes personalized features from a specific client. Theoretically, we show that \texttt{FGPR} converges to a critical point of the full log-likelihood function, subject to statistical error. Through extensive case studies we show that \texttt{FGPR} excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_14008 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling Yue, Xubo Kontar, Raed Al Machine Learning In this paper, we propose \texttt{FGPR}: a Federated Gaussian process ($\mathcal{GP}$) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local client computations. Notably, the resulting global model excels in personalization as \texttt{FGPR} jointly learns a global $\mathcal{GP}$ prior across all clients. The predictive posterior then is obtained by exploiting this prior and conditioning on local data which encodes personalized features from a specific client. Theoretically, we show that \texttt{FGPR} converges to a critical point of the full log-likelihood function, subject to statistical error. Through extensive case studies we show that \texttt{FGPR} excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling. |
| title | Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2111.14008 |