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Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systems

arXiv 2609.19337 ↗First posted 16 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

We present Personalized Federated Hierarchical Gaussian Processes (pFedHGP) for probabilistic regression and classification when data are distributed across heterogeneous clients. Each client's latent function decomposes into (i) a shared global component, (ii) a client-specific deviation that shares the global kernel structure, and (iii) a flexible local residual. Sparse inducing-variable approximations and federated variational inference keep raw data local while the server synchronizes only low-dimensional statistics for the shared component. Full predictive distributions support uncertainty-aware decisions. In application studies, pFedHGP attains perfect fault classification in press tonnage monitoring using 13.77% of labeled cycles and recovers geographic zones in federated air-quality modeling without centralizing station-level time series. An Instantaneous Linear Mixing Model viewpoint links the hierarchy to multi-output Gaussian processes for correlated sensors.

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References · 20

  1. 2020Adaptive Personalized Federated Learningcited by 353
  2. 2013Gaussian Process Kernels for Pattern Discovery and Extrapolationcited by 131
  3. 2015Gaussian Processes for Data-Efficient Learning in Robotics and Controlcited by 97
  4. 2015Distributed Gaussian Processescited by 84
  5. 2021Personalized Federated Learning with Gaussian Processescited by 76
  6. 2018Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regressioncited by 33
  7. 2019Scalable Exact Inference in Multi-Output Gaussian Processescited by 25
  8. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  9. 2020The role of surrogate models in the development of digital twins of dynamic systemscited by 20
  10. 2014Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Modelscited by 19
  11. 2022Personalized PCA: Decoupling Shared and Unique Featurescited by 17
  12. 2014Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regressioncited by 15
8 more
  1. 2022Federated Data Analytics: A Study on Linear Modelscited by 9
  2. 2017Asynchronous Distributed Variational Gaussian Processes for Regressioncited by 7
  3. 2022Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Processcited by 5
  4. 2022Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learningcited by 4
  5. 2023Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applicationscited by 4
  6. 2024Federated Automatic Latent Variable Selection in Multi-output Gaussian Processescited by 3
  7. 2023Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Datacited by 2
  8. 2025Robust, Online, and Adaptive Decentralized Gaussian Processescited by 2

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