标题： 具有拜占庭容错的高斯过程回归联邦在线学习 显示英文标题

标题： Byzantine-resilient federated online learning for Gaussian process regression

摘要： 在本文中，我们研究了针对高斯过程回归（GPR）的拜占庭容错联邦在线学习。我们开发了一个拜占庭容错的联邦GPR算法，允许云和一组代理共同学习一个潜在函数，并在一些代理出现拜占庭故障（即任意且可能具有对抗性的行为）时提高学习性能。每个基于代理的局部GPR将可能被破坏的局部预测发送到云，基于云的聚合GPR通过一个拜占庭容错的专家产品聚合规则计算一个全局模型。然后，云将当前的全局模型广播给所有代理。基于代理的融合GPR通过融合接收到的全局模型与基于代理的局部GPR的模型来改进局部预测。此外，我们量化了基于代理的融合GPR相对于基于代理的局部GPR的学习准确度提升。我们在一个玩具示例和两个中等规模的真实世界数据集上进行了实验，以展示所提出算法的性能。 显示英文摘要

摘要： In this paper, we study Byzantine-resilient federated online learning for Gaussian process regression (GPR). We develop a Byzantine-resilient federated GPR algorithm that allows a cloud and a group of agents to collaboratively learn a latent function and improve the learning performances where some agents exhibit Byzantine failures, i.e., arbitrary and potentially adversarial behavior. Each agent-based local GPR sends potentially compromised local predictions to the cloud, and the cloud-based aggregated GPR computes a global model by a Byzantine-resilient product of experts aggregation rule. Then the cloud broadcasts the current global model to all the agents. Agent-based fused GPR refines local predictions by fusing the received global model with that of the agent-based local GPR. Moreover, we quantify the learning accuracy improvements of the agent-based fused GPR over the agent-based local GPR. Experiments on a toy example and two medium-scale real-world datasets are conducted to demonstrate the performances of the proposed algorithm.