论文标题
完全分散的，可扩展的高斯流程，用于多机构联合学习
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning
论文作者
论文摘要
在本文中，我们提出了用于高斯过程（GP）培训和多代理系统预测的分散和可扩展算法。为了分散GP培训优化算法的实施，我们采用了乘数的交替方向方法（ADMM）。为GP高参数训练提供了分散的近端ADMM的封闭式解决方案，并具有最大的似然估计。通过使用迭代和共识方法，用于GP预测的多种聚集技术被分散。此外，我们提出了一个基于协方差的最近的邻居选择策略，该策略使一部分代理能够执行预测。通过有关合成和真实数据的数值实验，说明了所提出方法的功效。
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.
