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

Code

• github.com/umdatasciencelab/federated_gaussian_process
  OfficialIn papernone★ 4

Abstract

In this paper, we propose FGPR: 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 FGPR 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 FGPR converges to a critical point of the full log-likelihood function, subject to statistical error. Through extensive case studies we show that FGPR excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling.

Tasks

Reproductions