Federated machine learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning. Being a widely distributed system, federated machine learning requires various system design thinking. To better design a federated machine learning system, researchers have introduced multiple patterns and tactics that cover various system design aspects. However, the multitude of patterns leaves the designers confused about when and which pattern to adopt. In this paper, we present a set of decision models for the selection of patterns for federated machine learning architecture design based on a systematic literature review on federated machine learning, to assist designers and architects who have limited knowledge of federated machine learning. Each decision model maps functional and non-functional requirements of federated machine learning systems to a set of patterns. We also clarify the drawbacks of the patterns. We evaluated the decision models by mapping the decision patterns to concrete federated machine learning architectures by big tech firms to assess the models' correctness and usefulness. The evaluation results indicate that the proposed decision models are able to bring structure to the federated machine learning architecture design process and help explicitly articulate the design rationale.

翻译：联邦机器学习在学术界和工业界正快速发展，作为解决机器学习中数据饥渴与隐私问题的方案。作为广泛分布的系统，联邦机器学习需要多种系统设计思维。为更好设计联邦机器学习系统，研究者引入了覆盖系统设计各层面的多种模式与策略。然而，繁多的模式使设计者在何时采用何种模式上感到困惑。本文基于对联邦机器学习的系统文献综述，提出一组用于选择联邦机器学习架构设计模式的决策模型，以协助对联邦机器学习知识有限的设计者与架构师。每个决策模型将联邦机器学习系统的功能性与非功能性需求映射至一组模式，同时阐明各模式的局限。我们通过将决策模式映射至大型科技企业的具体联邦机器学习架构来评估模型正确性与实用性。评估结果表明，所提出的决策模型能够为联邦机器学习架构设计过程带来结构性，并帮助明确阐述设计依据。