The unprecedented demand for collaborative machine learning in a privacy-preserving manner gives rise to a novel machine learning paradigm called federated learning (FL). Given a sufficient level of privacy guarantees, the practicality of an FL system mainly depends on its time-to-accuracy performance during the training process. Despite bearing some resemblance with traditional distributed training, FL has four distinct challenges that complicate the optimization towards shorter time-to-accuracy: information deficiency, coupling for contrasting factors, client heterogeneity, and huge configuration space. Motivated by the need for inspiring related research, in this paper we survey highly relevant attempts in the FL literature and organize them by the related training phases in the standard workflow: selection, configuration, and reporting. We also review exploratory work including measurement studies and benchmarking tools to friendly support FL developers. Although a few survey articles on FL already exist, our work differs from them in terms of the focus, classification, and implications.

翻译：前所未有的以保护隐私的方式合作机器学习的需求产生了一种新型机器学习模式,称为联合学习(FL)。鉴于有足够的隐私保障,FL系统的实用性主要取决于培训过程中的时间到准确性表现。尽管与传统的分布式培训有些相似,但FL有四个截然不同的挑战,使缩短时间到准确性的优化复杂化:信息不足、将对比因素、客户差异性和庞大的配置空间结合起来。出于激励性相关研究的需要,我们在本文件中调查了FL文献中非常相关的尝试,并在标准工作流程(选择、配置和报告)的相关培训阶段组织这些尝试。我们还审查了包括衡量研究和基准工具在内的探索性工作,以友好支持FL开发者。虽然关于FL的一些调查文章已经存在,但我们的工作在重点、分类和影响方面与它们不同。