mlsanityData to 7 Oct 2026

A First Look at Deep Learning Apps on Smartphones

arXiv 1812.05448 ↗First posted 8 Nov 2018cs.LG cs.CYcited by 47

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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.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Nov 2018.

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Citing papers per year: 2021: 11; 2022: 10; 2023: 8; 2024: 10; 2025: 5; 2026: 3.

47 papers.

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  1. 2024Fast On-device LLM Inference with NPUscited by 58
  2. 2021DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injectioncited by 42
  3. 2024MELTing point: Mobile Evaluation of Language Transformerscited by 35
  4. 2022Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customizationcited by 34
  5. 2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applicationscited by 32
  6. 2021FEDZIP: A Compression Framework for Communication-Efficient Federated Learningcited by 27
  7. 2021Smart at what cost? Characterising Mobile Deep Neural Networks in the wildcited by 25
  8. 2023Mobile Foundation Model as Firmwarecited by 25
  9. 2021An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applicationscited by 20
  10. 2021LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Visioncited by 20
  11. 2021Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Appscited by 19
  12. 2022On-device Training: A First Overview on Existing Systemscited by 18
35 more
  1. 2024PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMscited by 18
  2. 2023NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscationcited by 15
  3. 2024Are LLMs Correctly Integrated into Software Systems?cited by 13
  4. 2022Benchmarking of DL Libraries and Models on Mobile Devicescited by 12
  5. 2022Smart App Attack: Hacking Deep Learning Models in Android Appscited by 12
  6. 2021OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devicescited by 7
  7. 2022Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learningcited by 7
  8. 2022Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature reviewcited by 6
  9. 2022Boosting DNN Cold Inference on Edge Devicescited by 6
  10. 2024Investigating White-Box Attacks for On-Device Modelscited by 6
  11. 2021Boosting Mobile CNN Inference through Semantic Memorycited by 5
  12. 2023PolyThrottle: Energy-efficient Neural Network Inference on Edge Devicescited by 5
  13. 2023Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devicescited by 4
  14. 2024Fairness Concerns in App Reviews: A Study on AI-based Mobile Appscited by 4
  15. 2024Generative AI on the Edge: Architecture and Performance Evaluationcited by 4
  16. 2025NestQuant: Post-Training Integer-Nesting Quantization for On-Device DNNcited by 4
  17. 2022Edge Security: Challenges and Issuescited by 3
  18. 2023Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Appscited by 2
  19. 2023Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challengescited by 2
  20. 2024A Picture is Worth 500 Labels: A Case Study of Demographic Disparities in Local Machine Learning Models for Instagram and TikTokcited by 2
  21. 2025THEMIS: Towards Practical Intellectual Property Protection for Post-Deployment On-Device Deep Learning Modelscited by 2
  22. 2025A Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devicescited by 2
  23. 2022An Empirical Study of AI Techniques in Mobile Applicationscited by 1
  24. 2024CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloadscited by 1
  25. 2025Stealthy Backdoor Attack to Real-world Models in Android Appscited by 1
  26. 2025Optimizing Storage Overhead of User Behavior Log for ML-embedded Mobile Appscited by 1
  27. 2021C3DRec: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Eracited by 0
  28. 2021Doing good by fighting fraud: Ethical anti-fraud systems for mobile paymentscited by 0
  29. 2021Scalable Smartphone Cluster for Deep Learningcited by 0
  30. 2023Mobiprox: Supporting Dynamic Approximate Computing on Mobilescited by 0
  31. 2023Towards Real Smart Apps: Investigating Human-AI Interactions in Smartphone On-Device AI Appscited by 0
  32. 2024MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via Automating Deep Neural Network Porting for Mobile Deploymentcited by 0
  33. 2026Optimizing Feature Extraction for On-device Model Inference with User Behavior Sequencescited by 0
  34. 2026LoREnc: Low-Rank Encryption for Securing Foundation Models and LoRA Adapterscited by 0
  35. 2026SoK: Attack and Defense Landscape of Mobile On-device AI Systemscited by 0