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

arXiv: https://arxiv.org/abs/1909.08329 · HTML: https://mlsanity.com/paper/1909.08329/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [A Comprehensive Survey of Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2106.15406/) (2021)
- [Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences](https://mlsanity.com/paper/2310.04621/) (2023)
- [On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems](https://mlsanity.com/paper/2201.10382/) (2022)
- [Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference](https://mlsanity.com/paper/2404.03085/) (2024)
- [Scheduling Algorithms for Federated Learning with Minimal Energy Consumption](https://mlsanity.com/paper/2209.06210/) (2022)
- [Experience Scaling: Post-Deployment Evolution For Large Language Models](https://mlsanity.com/paper/2509.18771/) (2025)
- [Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning](https://mlsanity.com/paper/2609.39250/) (2026)

## Abstract (arXiv)

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.