Byzantine-tolerant federated Gaussian process regression for streaming data
Byzantine-tolerant federated Gaussian process regression for streaming data
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发表时间：
2022
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通讯作者：
Xu Zhang;Zhenyuan Yuan;Minghui Zhu
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作者：
Xu Zhang;Zhenyuan Yuan;Minghui Zhu
在本文中，我们考虑拜占庭容忍联邦学习流数据使用高斯过程回归（GPR）。特别是，一个云和一组代理的目标是协同学习一个潜在的功能，其中一些代理受到拜占庭攻击。本文提出了一种容忍拜占庭的联邦探地雷达算法，该算法包括三个模块：基于Agent的本地探地雷达、基于云的聚合探地雷达和基于Agent的融合探地雷达。我们推导出基于云的聚合GPR的平均值与目标函数之间的预测误差的上界，前提是拜占庭代理小于所有代理的四分之一。我们还刻画了预测方差的上下限。在一个人工数据集和两个真实数据集上进行了实验，以评估所提出的算法。
In this paper, we consider Byzantine-tolerant federated learning for streaming data using Gaussian process regression (GPR). In particular, a cloud and a group of agents aim to collaboratively learn a latent function where some agents are subject to Byzantine attacks. We develop a Byzantine-tolerant federated GPR algorithm, which includes three modules: agent-based local GPR, cloud-based aggregated GPR and agent-based fused GPR. We derive the upper bounds on the prediction error between the mean from the cloud-based aggregated GPR and the target function provided that Byzantine agents are less than one quarter of all the agents. We also characterize the lower and upper bounds of the predictive variance. Experiments on a synthetic dataset and two real-world datasets are conducted to evaluate the proposed algorithm.