抗拜占庭故障的联邦在线高斯过程学习，提升分布式模型鲁棒性。
Byzantine-resilient federated online learning for Gaussian process regression
- 云端用抗干扰聚合规则融合各节点预测，抵御恶意数据
- 实验显示融合后模型精度显著优于本地模型
- 适合存在异常节点的分布式机器学习场景
本文研究抗拜占庭故障的联邦在线高斯过程回归（GPR）。提出一种抗拜占庭的联邦GPR算法，使云端与多个代理协同学习隐含函数，在部分代理出现任意甚至恶意行为（即拜占庭故障）时仍能保持性能。每个代理基于本地GPR生成可能被污染的预测并发送至云端；云端采用抗拜占庭的专家乘积聚合规则构建全局模型，并广播给所有代理；代理再通过融合接收的全局模型与本地模型，更新自身预测。进一步量化了融合模型相对于本地模型的学习精度提升。在模拟示例及两个中等规模真实数据集上进行了实验，验证了所提算法的有效性。
原文摘要 · Abstract (English)
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.
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