The paper introduces Personalized Federated Hierarchical Gaussian Processes (pFedHGP), a method for probabilistic regression and classification on data distributed across heterogeneous clients. Each client’s latent function is split into a shared global component, a client‑specific deviation that shares the global kernel, and a flexible local residual. Using sparse inducing‑variable approximations and federated variational inference, raw data remain local while the server exchanges only low‑dimensional statistics, enabling full predictive distributions for uncertainty‑aware decisions. In experiments, pFedHGP achieves perfect fault classification in press tonnage monitoring with only 13.77% of labeled cycles and accurately recovers geographic zones in federated air‑quality modeling without centralizing station‑level time series.
arXiv:2610.03201v1 Announce Type: cross Abstract: Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitione...
arXiv:2608. 16606v1 Announce Type: new Abstract: Outliers can substantially distort Gaussian process regression (GPR) due to its conventional Gaussian observation likelihood, leading to inaccurate model learning and prediction.
arXiv:2609.22654v1 Announce Type: cross Abstract: Federated learning (FL) has emerged as a leading privacy-preserving framework for collaborative machine learning across decentralized environments. W...
arXiv:2303. 04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients.
The paper introduces COIN-GP, an observer‑based cooperative learning framework for distributed sensor networks that jointly estimates system states and partially unknown dynamics using online distributed Gaussian Process regression. It addresses challenges of incomplete state observations and deficient GP models by proposing a novel data collection strategy with theoretical feasibility conditions. The authors also derive an error upper bound for both state and model estimation and demonstrate through simulations that COIN‑GP outperforms existing distributed GP‑based methods.
The paper introduces two distributed recursive Gaussian process algorithms, ADMM‑RGP and PDMM‑RGP, designed for multi‑output regression in multi‑agent systems. It analyzes their stability and convergence, proposes parameter selection strategies to speed up convergence, and demonstrates that the methods reduce communication overhead while preserving estimation accuracy and consensus. Experiments on a real‑world wind dataset confirm the algorithms’ effectiveness across different communication graph connectivities.
arXiv:2610. 13631v1 Announce Type: new Abstract: Fault diagnosis in asset-intensive industries requires multiclass models trained on noisy, sensitive data collected across geographically distributed client sites.
arXiv:2602. 23006v2 Announce Type: replace-cross Abstract: Simulating a Gaussian process requires sampling from a high-dimensional Gaussian distribution, which scales cubically with the number of sample locations.
arXiv:2607. 00051v1 Announce Type: cross Abstract: Accurate modeling of wind turbine power curves is crucial for optimal wind farm operation.
arXiv:2608. 11917v1 Announce Type: new Abstract: Multi-output Gaussian process regression scales cubically in the number of observations times outputs, and dense kernel-matrix methods need bespoke handling whenever different outputs are observed at different inputs.
The paper presents a large‑dimensional analysis of Generalized Total Variation Minimization (GTVMin) for least‑squares federated learning. It shows that Lipschitz observations of the global GTVMin solution concentrate in high dimensions regardless of network structure, and derives exact analytic expressions for performance under both i.i.d. and non‑i.i.d. Gaussian mixture models. These results quantify how regularization balances local adaptation against collaboration, and are validated by experiments on synthetic and real‑world data.
The paper introduces COIN-GP, an observer‑based dynamic cooperative learning framework for distributed sensor networks that jointly estimates system states and partially unknown dynamics when only partial state observations are available. It employs online distributed Gaussian Process regression and a novel data‑collection strategy with theoretical feasibility conditions, and derives an error upper bound that combines state and model estimation errors. Simulations show that COIN‑GP outperforms existing distributed GP‑based methods.