# Deep Learning Towards Mobile Applications

arXiv: https://arxiv.org/abs/1809.03559 · HTML: https://mlsanity.com/paper/1809.03559/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 16 of 16)

- [DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization](https://mlsanity.com/paper/2209.05227/) (2022)
- [An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications](https://mlsanity.com/paper/2101.04930/) (2021)
- [Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps](https://mlsanity.com/paper/2101.04401/) (2021)
- [Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs](https://mlsanity.com/paper/2403.20041/) (2024)
- [Smart App Attack: Hacking Deep Learning Models in Android Apps](https://mlsanity.com/paper/2204.11075/) (2022)
- [p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks](https://mlsanity.com/paper/2111.14837/) (2021)
- [Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps](https://mlsanity.com/paper/2305.03963/) (2023)
- [EdgeOL: Efficient in-situ Online Learning on Edge Devices](https://mlsanity.com/paper/2401.16694/) (2024)
- [THEMIS: Towards Practical Intellectual Property Protection for Post-Deployment On-Device Deep Learning Models](https://mlsanity.com/paper/2503.23748/) (2025)
- [On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detection](https://mlsanity.com/paper/2304.02181/) (2023)
- ["Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications](https://mlsanity.com/paper/2503.03927/) (2025)
- [LightFuse: Lightweight CNN based Dual-exposure Fusion](https://mlsanity.com/paper/2107.02299/) (2021)
- [Dynamic Neural Network Architectural and Topological Adaptation and Related Methods -- A Survey](https://mlsanity.com/paper/2108.10066/) (2021)
- [Progressive Transmission and Inference of Deep Learning Models](https://mlsanity.com/paper/2110.00916/) (2021)
- [Towards Implementing Energy-aware Data-driven Intelligence for Smart Health Applications on Mobile Platforms](https://mlsanity.com/paper/2302.00514/) (2023)
- [High-Level Parallelism and Nested Features for Dynamic Inference Cost and Top-Down Attention](https://mlsanity.com/paper/2308.05128/) (2023)

## Abstract (arXiv)

Recent years have witnessed an explosive growth of mobile devices. Mobile devices are permeating every aspect of our daily lives. With the increasing usage of mobile devices and intelligent applications, there is a soaring demand for mobile applications with machine learning services. Inspired by the tremendous success achieved by deep learning in many machine learning tasks, it becomes a natural trend to push deep learning towards mobile applications. However, there exist many challenges to realize deep learning in mobile applications, including the contradiction between the miniature nature of mobile devices and the resource requirement of deep neural networks, the privacy and security concerns about individuals' data, and so on. To resolve these challenges, during the past few years, great leaps have been made in this area. In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning. The former two aspects cover the primary tasks of deep learning. Then, we go through our two recent applications that apply the data collected by mobile devices to inferring mood disturbance and user identification. Finally, we conclude this paper with the discussion of the future of this area.