# Moving Deep Learning into Web Browser: How Far Can We Go?

arXiv: https://arxiv.org/abs/1901.09388 · HTML: https://mlsanity.com/paper/1901.09388/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2019.

## Built on by (most cited first, 14 of 14)

- [An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications](https://mlsanity.com/paper/2101.04930/) (2021)
- [Understanding Performance Problems in Deep Learning Systems](https://mlsanity.com/paper/2112.01771/) (2021)
- [Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning](https://mlsanity.com/paper/2201.05286/) (2022)
- [Boosting DNN Cold Inference on Edge Devices](https://mlsanity.com/paper/2206.07446/) (2022)
- [Anatomizing Deep Learning Inference in Web Browsers](https://mlsanity.com/paper/2402.05981/) (2024)
- [Empowering In-Browser Deep Learning Inference on Edge Devices with Just-in-Time Kernel Optimizations](https://mlsanity.com/paper/2309.08978/) (2023)
- [Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges](https://mlsanity.com/paper/2308.12438/) (2023)
- [WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy](https://mlsanity.com/paper/2110.11646/) (2021)
- [Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge](https://mlsanity.com/paper/2211.07130/) (2022)
- [C3DRec: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era](https://mlsanity.com/paper/2101.05641/) (2021)
- [InFL-UX: A Toolkit for Web-Based Interactive Federated Learning](https://mlsanity.com/paper/2503.04318/) (2025)
- [Decentralized Direct Volume Rendering: A Browser-Native GPU Architecture for MRI Digital Twins in Resource-Constrained Settings](https://mlsanity.com/paper/2605.19737/) (2026)
- [GenomeOcean Anywhere: Private WebGPU Inference for Genome MoEs](https://mlsanity.com/paper/2609.35882/) (2026)
- [Privacy-Friendly Cohort Determination: Sealed, CSP-Independent In-Browser ML Inference of Professional Segments for Identity-Less Advertising](https://mlsanity.com/paper/2609.36153/) (2026)

## Abstract (arXiv)

Recently, several JavaScript-based deep learning frameworks have emerged, making it possible to perform deep learning tasks directly in browsers. However, little is known on what and how well we can do with these frameworks for deep learning in browsers. To bridge the knowledge gap, in this paper, we conduct the first empirical study of deep learning in browsers. We survey 7 most popular JavaScript-based deep learning frameworks, investigating to what extent deep learning tasks have been supported in browsers so far. Then we measure the performance of different frameworks when running different deep learning tasks. Finally, we dig out the performance gap between deep learning in browsers and on native platforms by comparing the performance of TensorFlow.js and TensorFlow in Python. Our findings could help application developers, deep-learning framework vendors and browser vendors to improve the efficiency of deep learning in browsers.