In Federated Learning (FL), multiple parties collaboratively train a shared Machine Learning model to encapsulate all private knowledge without exchange of information. While it has seen application in several industrial projects, most FL research considers simulations on a central machine, without considering potential hardware heterogeneity between the involved parties. In this paper, we present BouquetFL, a framework designed to address this methodological gap by simulating heterogeneous client hardware on a single physical machine. By programmatically emulating diverse hardware configurations through resource restriction, BouquetFL enables controlled FL experimentation under realistic hardware diversity. Our tool provides an accessible way to study system heterogeneity in FL without requiring multiple physical devices, thereby bringing experimental practice closer to practical deployment conditions. The target audience are FL researchers studying highly heterogeneous federations. We include a wide range of profiles derived from commonly available consumer and small-lab devices, as well as a custom hardware sampler built on real-world hardware popularity, allowing users to configure the federation according to their preference.

翻译：在联邦学习（FL）中，多方协作训练一个共享的机器学习模型，以整合所有私有知识而无需交换信息。尽管已在多个工业项目中得到应用，但大多数FL研究考虑在中央机器上进行模拟，未考虑参与方之间潜在的硬件异构性。本文提出BouquetFL框架，旨在通过在单台物理机器上模拟异构客户端硬件来解决这一方法学差距。通过资源限制以编程方式模拟多样化硬件配置，BouquetFL能够在现实的硬件多样性下实现受控的FL实验。该工具为研究FL中的系统异构性提供了一种便捷途径，无需多台物理设备，从而使实验实践更贴近实际部署条件。目标受众为研究高度异构联邦的FL研究人员。我们提供了源自常用消费级和小型实验室设备的广泛硬件配置档案，以及基于真实世界硬件普及度构建的自定义硬件采样器，允许用户根据偏好配置联邦系统。