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.
@article{arxiv.2203.02865,
title = {Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning},
author = {George P. Kontoudis and Daniel J. Stilwell},
journal= {arXiv preprint arXiv:2203.02865},
year = {2022}
}