In recent years, mobile devices have gained increasing development with stronger computation capability and larger storage space. Some of the computation-intensive machine learning tasks can now be run on mobile devices. To exploit the resources available on mobile devices and preserve personal privacy, the concept of client-based machine learning has been proposed. It leverages the users’ local hardware and local data to solve machine learning sub-problems on mobile devices and only uploads computation results rather than the original data for the optimization of the global model. Such an architecture can not only relieve computation and storage burdens on servers but also protect the users’ sensitive information. Another benefit is the bandwidth reduction because various kinds of local data can be involved in the training process without being uploaded. In this article, we provide a literature review on the progressive development of machine learning from server based to client based. We revisit a number of widely used server-based and client-based machine learning methods and applications. We also extensively discuss the challenges and future directions in this area. We believe that this survey will give a clear overview of client-based machine learning and provide guidelines on applying client-based machine learning to practice.

中文翻译：

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从基于服务器到基于客户端的机器学习

近年来，移动设备得到了越来越多的发展，计算能力越来越强，存储空间也越来越大。一些计算密集型机器学习任务现在可以在移动设备上运行。为了利用移动设备上可用的资源并保护个人隐私，已经提出了基于客户端的机器学习的概念。它利用用户的本地硬件和本地数据来解决移动设备上的机器学习子问题，并且只上传计算结果而不是原始数据来优化全局模型。这样的架构不仅可以减轻服务器的计算和存储负担，还可以保护用户的敏感信息。另一个好处是带宽减少，因为各种本地数据可以在不上传的情况下参与训练过程。在本文中，我们对机器学习从基于服务器到基于客户端的逐步发展进行了文献综述。我们重新审视了一些广泛使用的基于服务器和基于客户端的机器学习方法和应用程序。我们还广泛讨论了该领域的挑战和未来方向。我们相信，本次调查将对基于客户端的机器学习进行清晰的概述，并为将基于客户端的机器学习应用于实践提供指导。我们重新审视了一些广泛使用的基于服务器和基于客户端的机器学习方法和应用程序。我们还广泛讨论了该领域的挑战和未来方向。我们相信，本次调查将对基于客户端的机器学习进行清晰的概述，并为将基于客户端的机器学习应用于实践提供指导。我们重新审视了一些广泛使用的基于服务器和基于客户端的机器学习方法和应用程序。我们还广泛讨论了该领域的挑战和未来方向。我们相信，本次调查将对基于客户端的机器学习进行清晰的概述，并为将基于客户端的机器学习应用于实践提供指导。