GUIDE-FBO：通过不确定性干预与分布交换实现联邦贝叶斯优化的引导

中文摘要 AI 辅助

联邦贝叶斯优化（FBO）使分布式智能体能够在不共享原始局部观测数据的情况下，协作优化昂贵的黑盒目标函数。然而，在通信约束和任务异质性下，有效的知识迁移仍然具有挑战性。我们提出GUIDE-FBO，其中智能体交换从其局部高斯过程（GP）后验推断出的各自最优位置上的紧凑分布，而非原始观测、查询点或代理参数。服务器在将子集返回给每个智能体之前，合并并重新加权这些分布组件。每个智能体随后构建一个联邦干预高斯过程（FI-GP），该过程保留局部后验均值并对其协方差进行空间重缩放，以用于局部决策。对于上置信界（UCB）实例化GUIDE-UCB，我们证明任何有界的FI-GP不确定性干预都能保持标准GP-UCB的领先阶累积遗憾率。当转移的分布在最优区域附近比在次优区域提供更大支持时，选择后者需要更大的局部后验不确定性。在12个合成基准和三个真实世界优化任务上的实验表明，GUIDE-FBO在从同质到严重异质的各种设置中均保持有效。消融结果突出了空间局部化不确定性干预的重要性，而通信分析表明GUIDE-FBO仅交换紧凑的分布消息。

英文摘要

Federated Bayesian Optimization (FBO) enables distributed agents to collaboratively optimize expensive black-box objectives without sharing raw local observations. However, effective knowledge transfer remains challenging under communication constraints and task heterogeneity. We propose GUIDE-FBO, in which agents exchange compact distributions over the locations of their respective optima inferred from local Gaussian process (GP) posteriors, rather than raw observations, query points, or surrogate parameters. The server merges and reweights these distributional components before returning a subset to each agent. Each agent then constructs a Federated Interventional GP (FI-GP), which preserves the local posterior mean and spatially rescales its covariance for local decision making. For the upper confidence bound (UCB) instantiation, GUIDE-UCB, we prove that any bounded FI-GP uncertainty intervention preserves the leading-order cumulative regret rate of standard GP-UCB. When the transferred distributions place greater support near an optimum than in a suboptimal region, selecting the latter requires greater local posterior uncertainty. Experiments on 12 synthetic benchmarks and three real-world optimization tasks show that GUIDE-FBO remains effective across settings ranging from homogeneous to severely heterogeneous. Ablation results highlight the importance of spatially localized uncertainty intervention, while the communication analysis shows that GUIDE-FBO exchanges only compact distributional messages.

发表机构

• Southern University of Science and Technology（南方科技大学）

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