提出三类协作联邦黑箱优化框架，解决隐私与异构难题。
Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective
- 分三类框架：集中协调、局部决策、预测协同，统一处理分布式优化
- 通过协作提升本地代理的预测性能，改善决策质量
- 适合需要隐私保护的工业优化场景，如多机构联合调参
我们聚焦于协作与联邦黑箱优化（BBOpt），其中各智能体通过协作的序列实验优化各自异构的黑箱函数。从贝叶斯优化视角出发，针对分布式实验、异质性与隐私等核心挑战，提出三个统一框架：(i) 全局框架，实验由中心协调；(ii) 局部框架，代理基于最少共享信息自主决策；(iii) 预测框架，通过协作增强本地代理模型以改进决策。我们将现有方法归入这些框架，并指出关键开放问题，以释放联邦BBOpt的潜力。目标是推动联邦学习从描述性/预测性范式转向处方性范式，尤其适用于本质为序列决策的BBOpt问题。
原文摘要 · Abstract (English)
We focus on collaborative and federated black-box optimization (BBOpt), where agents optimize their heterogeneous black-box functions through collaborative sequential experimentation. From a Bayesian optimization perspective, we address the fundamental challenges of distributed experimentation, heterogeneity, and privacy within BBOpt, and propose three unifying frameworks to tackle these issues: (i) a global framework where experiments are centrally coordinated, (ii) a local framework that allows agents to make decisions based on minimal shared information, and (iii) a predictive framework that enhances local surrogates through collaboration to improve decision-making. We categorize existing methods within these frameworks and highlight key open questions to unlock the full potential of federated BBOpt. Our overarching goal is to shift federated learning from its predominantly descriptive/predictive paradigm to a prescriptive one, particularly in the context of BBOpt - an inherently sequential decision-making problem.
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