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

• URL: http://arxiv.org/abs/2203.02865v1
• Date: Sun, 6 Mar 2022 02:54:13 GMT
• Title: Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning
• Authors: George P. Kontoudis, Daniel J. Stilwell
• Abstract summary: We propose decentralized and scalable algorithms for GP training and prediction in multi-agent systems. The efficacy of the proposed methods is illustrated with numerical experiments on synthetic and real data.
• Score: 14.353574903736343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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