Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results and identify future trends to encourage researchers to advance their current work.

中文翻译：

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联邦机器学习的系统文献综述

联邦学习是一种新兴的机器学习范式，客户在本地训练模型并根据本地模型更新制定全局模型。为了确定联邦学习的最新技术并探索如何开发联邦学习系统，我们基于 231 项主要研究从软件工程的角度进行了系统的文献回顾。我们的数据综合涵盖了联邦学习系统开发的生命周期，包括背景理解、需求分析、架构设计、实施和评估。我们突出并总结了结果中的发现，并确定了未来的趋势，以鼓励研究人员推进他们目前的工作。