{"slug": "personalized-federated-hierarchical-gaussian-processes-for-privacy-preserving-of", "title": "Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systems", "summary": "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.", "body_md": "arXiv:2609.19337v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/personalized-federated-hierarchical-gaussian-processes-for-privacy-preserving-of", "canonical_source": "https://arxiv.org/abs/2609.19337", "published_at": "2026-09-18 04:00:00+00:00", "updated_at": "2026-09-18 04:23:30.911861+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "ai-safety"], "entities": ["Personalized Federated Hierarchical Gaussian Processes", "pFedHGP", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/personalized-federated-hierarchical-gaussian-processes-for-privacy-preserving-of", "markdown": "https://wpnews.pro/news/personalized-federated-hierarchical-gaussian-processes-for-privacy-preserving-of.md", "text": "https://wpnews.pro/news/personalized-federated-hierarchical-gaussian-processes-for-privacy-preserving-of.txt", "jsonld": "https://wpnews.pro/news/personalized-federated-hierarchical-gaussian-processes-for-privacy-preserving-of.jsonld"}}