  In this paper, we propose decentralized and scalable algorithms for Gaussian
process (GP) training and prediction in multi-agent systems. To decentralize
the implementation of GP training optimization algorithms, we employ the
alternating direction method of multipliers (ADMM). A closed-form solution of
the decentralized proximal ADMM is provided for the case of GP hyper-parameter
training with maximum likelihood estimation. Multiple aggregation techniques
for GP prediction are decentralized with the use of iterative and consensus
methods. In addition, we propose a covariance-based nearest neighbor selection
strategy that enables a subset of agents to perform predictions. The efficacy
of the proposed methods is illustrated with numerical experiments on synthetic
and real data.
