Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
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In this paper, we propose : a Federated Gaussian process (𝒢𝒫) 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 𝒢𝒫 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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