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arXiv · 2203.02865

Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning

George P. Kontoudis·Daniel J. Stilwell
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Abstract

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.

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BibTeXRIS

George P. Kontoudis, Daniel J. Stilwell. 2022-03-06. Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning. https://arxiv.org/abs/2203.02865

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