@misc{kontoudis2022fully,
  title = {Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning},
  author = {Kontoudis, George P and Stilwell, Daniel J},
  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.},
  keywords={Gaussian processes, distributed optimization, multi-agent systems, decentralized networks,gradient-based optimization},
  howpublished = {arXiv preprint arXiv:2203.02865},
  year = {2022},
  url_pdf = {https://arxiv.org/pdf/2203.02865.pdf},
  url_html = {https://arxiv.org/abs/2203.02865}
}

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