From Server-Based to Client-Based Machine Learning: A Comprehensive Survey

TL;DR

This survey reviews the evolution from server-based to client-based machine learning, highlighting methods, applications, challenges, and future directions for deploying ML on mobile devices while preserving privacy.

Contribution

It provides a comprehensive overview of client-based machine learning, comparing it with traditional server-based approaches, and discusses practical guidelines and future research directions.

Findings

Client-based ML reduces server load and preserves privacy.

Mobile devices can now handle computation-intensive ML tasks.

Challenges include data heterogeneity and resource constraints.

Abstract

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.