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.

本研究关注协作和联邦黑箱优化（BBOpt），解决代理在协作序列实验中优化异质黑箱函数所面临的分布式实验、异质性和隐私等基本挑战。提出了三个统一框架，包括集中协调的全球框架、基于最小共享信息的局部框架以及通过协作增强局部代理的预测框架，以改善决策。研究的关键发现是，推动联邦学习从主要的描述/预测模式转向更具指导性的模式，尤其是在BBOpt这一固有的序列决策问题上。

协作与联邦黑箱优化：贝叶斯优化视角