# A First Look at Deep Learning Apps on Smartphones

arXiv: https://arxiv.org/abs/1812.05448 · HTML: https://mlsanity.com/paper/1812.05448/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2018.

## Built on by (most cited first, 30 of 47)

- [Fast On-device LLM Inference with NPUs](https://mlsanity.com/paper/2407.05858/) (2024)
- [DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection](https://mlsanity.com/paper/2101.06896/) (2021)
- [MELTing point: Mobile Evaluation of Language Transformers](https://mlsanity.com/paper/2403.12844/) (2024)
- [Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization](https://mlsanity.com/paper/2203.09747/) (2022)
- [Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications](https://mlsanity.com/paper/2204.11786/) (2022)
- [FEDZIP: A Compression Framework for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2102.01593/) (2021)
- [Smart at what cost? Characterising Mobile Deep Neural Networks in the wild](https://mlsanity.com/paper/2109.13963/) (2021)
- [Mobile Foundation Model as Firmware](https://mlsanity.com/paper/2308.14363/) (2023)
- [An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications](https://mlsanity.com/paper/2101.04930/) (2021)
- [LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision](https://mlsanity.com/paper/2112.09852/) (2021)
- [Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps](https://mlsanity.com/paper/2101.04401/) (2021)
- [On-device Training: A First Overview on Existing Systems](https://mlsanity.com/paper/2212.00824/) (2022)
- [PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs](https://mlsanity.com/paper/2407.01031/) (2024)
- [NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation](https://mlsanity.com/paper/2305.00097/) (2023)
- [Are LLMs Correctly Integrated into Software Systems?](https://mlsanity.com/paper/2407.05138/) (2024)
- [Benchmarking of DL Libraries and Models on Mobile Devices](https://mlsanity.com/paper/2202.06512/) (2022)
- [Smart App Attack: Hacking Deep Learning Models in Android Apps](https://mlsanity.com/paper/2204.11075/) (2022)
- [OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices](https://mlsanity.com/paper/2106.04723/) (2021)
- [Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning](https://mlsanity.com/paper/2201.05286/) (2022)
- [Boosting Mobile CNN Inference through Semantic Memory](https://mlsanity.com/paper/2112.02644/) (2021)
- [Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review](https://mlsanity.com/paper/2205.01081/) (2022)
- [Boosting DNN Cold Inference on Edge Devices](https://mlsanity.com/paper/2206.07446/) (2022)
- [Investigating White-Box Attacks for On-Device Models](https://mlsanity.com/paper/2402.05493/) (2024)
- [PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices](https://mlsanity.com/paper/2310.19991/) (2023)
- [Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devices](https://mlsanity.com/paper/2306.11426/) (2023)
- [Fairness Concerns in App Reviews: A Study on AI-based Mobile Apps](https://mlsanity.com/paper/2401.08097/) (2024)
- [Generative AI on the Edge: Architecture and Performance Evaluation](https://mlsanity.com/paper/2411.17712/) (2024)
- [NestQuant: Post-Training Integer-Nesting Quantization for On-Device DNN](https://mlsanity.com/paper/2506.17870/) (2025)
- [Edge Security: Challenges and Issues](https://mlsanity.com/paper/2206.07164/) (2022)
- [Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps](https://mlsanity.com/paper/2305.03963/) (2023)

## Abstract (arXiv)

We are in the dawn of deep learning explosion for smartphones. To bridge the gap between research and practice, we present the first empirical study on 16,500 the most popular Android apps, demystifying how smartphone apps exploit deep learning in the wild. To this end, we build a new static tool that dissects apps and analyzes their deep learning functions. Our study answers threefold questions: what are the early adopter apps of deep learning, what do they use deep learning for, and how do their deep learning models look like. Our study has strong implications for app developers, smartphone vendors, and deep learning R&D. On one hand, our findings paint a promising picture of deep learning for smartphones, showing the prosperity of mobile deep learning frameworks as well as the prosperity of apps building their cores atop deep learning. On the other hand, our findings urge optimizations on deep learning models deployed on smartphones, the protection of these models, and validation of research ideas on these models.