Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
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In this paper, we propose : a Federated Gaussian process (š¢š«) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local client computations. Notably, the resulting global model excels in personalization as jointly learns a global š¢š« prior across all clients. The predictive posterior then is obtained by exploiting this prior and conditioning on local data which encodes personalized features from a specific client. Theoretically, we show that converges to a critical point of the full log-likelihood function, subject to statistical error. Through extensive case studies we show that excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling.
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