Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Gaussian Process: Convergence, Automatic Personalization and
  Multi-fidelity Modeling   In this paper, we propose \texttt{FGPR}: a Federated Gaussian process
($\mathcal{GP}$) 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 \texttt{FGPR}
jointly learns a global $\mathcal{GP}$ 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 \texttt{FGPR} converges to a critical point of the full
log-likelihood function, subject to statistical error. Through extensive case
studies we show that \texttt{FGPR} excels in a wide range of applications and
is a promising approach for privacy-preserving multi-fidelity data modeling.

9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas