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

Researchers introduced Personalized Federated Hierarchical Gaussian Processes (pFedHGP), a federated learning method that decomposes each client's latent function into a shared global component, a client-specific deviation sharing the global kernel structure, and a flexible local residual, according to a new arXiv paper (2609.19337v1). Using sparse inducing-variable approximations and federated variational inference, pFedHGP keeps raw data local while the server synchronizes only low-dimensional statistics for the shared component. In application studies, pFedHGP achieved perfect fault classification in press tonnage monitoring using 13.77% of labeled cycles and recovered geographic zones in federated air-quality modeling without centralizing station-level time series.

arXiv:2609.19337v1 Announce Type: new Abstract: 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.