mlsanityData to 7 Oct 2026

Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling

arXiv 2111.14008 ↗First posted 28 Nov 2021stat.ML cs.LGcited by 8

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Abstract (arXiv)

In this paper, we propose textttFGPR: a Federated Gaussian process (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 textttFGPR jointly learns a global 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 textttFGPR converges to a critical point of the full log-likelihood function, subject to statistical error. Through extensive case studies we show that textttFGPR excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 47 of its 92 references that are in the map. First posted on arXiv 28 Nov 2021.

Builds on

47 of 92 references.

References · 47

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35 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 3; 2026: 1.

8 papers.

  1. 2022Federated Data Analytics: A Study on Linear Modelscited by 9
  2. 2024Federated Automatic Latent Variable Selection in Multi-output Gaussian Processescited by 3
  3. 2023Practical Privacy-Preserving Gaussian Process Regression via Secret Sharingcited by 2
  4. 2025Fed-Joint: Joint Modeling of Nonlinear Degradation Signals and Failure Events for Remaining Useful Life Prediction using Federated Learningcited by 2
  5. 2026Collaborative Contextual Bayesian Optimizationcited by 1
  6. 2023Learning Driver Models for Automated Vehicles via Knowledge Sharing and Personalizationcited by 0
  7. 2025Scalable Asynchronous Federated Modeling for Spatial Datacited by 0
  8. 2025R^2-HGP: A Double-Regularized Gaussian Process for Heterogeneous Transfer Learningcited by 0