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.

翻译：本文聚焦于协作与联邦黑盒优化问题，其中多个智能体通过协作式序贯实验来优化各自异构的黑盒函数。从贝叶斯优化视角出发，我们针对BBOpt中的分布式实验、异构性与隐私保护等核心挑战，提出了三个统一框架以应对这些问题：（i）全局框架，其中实验由中央协调；（ii）局部框架，允许智能体基于最小共享信息自主决策；（iii）预测框架，通过协作增强局部代理模型以改进决策质量。我们将现有方法归类于这些框架之下，并指出关键开放性问题以释放联邦BBOpt的全部潜力。我们的总体目标是将联邦学习从当前以描述/预测为主的研究范式转向处方性范式，特别是在BBOpt——这一本质上的序贯决策问题——的语境中实现这一转变。