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

In this paper, we propose
FGPR
: a Federated Gaussian process (
$\mathcal {GP}$
) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local computations. Notably, the resulting global model excels in personalization as
FGPR
jointly learns a shared prior across all devices. The predictive posterior is then obtained by exploiting this shared prior and conditioning on local data, which encodes personalized features from a specific dataset. Theoretically, we show that
FGPR
converges to a critical point of the full log-marginal likelihood function, subject to statistical errors. This result offers standalone value as it brings federated learning theoretical results to correlated paradigms. Through extensive case studies on several regression tasks, we show that
FGPR
excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling.