# DistML.js: Installation-free Distributed Deep Learning Framework for Web Browsers

arXiv: https://arxiv.org/abs/2407.01023 · HTML: https://mlsanity.com/paper/2407.01023/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel](https://mlsanity.com/paper/2304.11277/) (2023)
- [TensorFlow.js: Machine Learning for the Web and Beyond](https://mlsanity.com/paper/1901.05350/) (2019)
- [BOINC: A Platform for Volunteer Computing](https://mlsanity.com/paper/1903.01699/) (2019)
- [JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training](https://mlsanity.com/paper/1910.07402/) (2019)
- [Development of JavaScript-based deep learning platform and application to distributed training](https://mlsanity.com/paper/1702.01846/) (2017)

## Built on by (most cited first, 1 of 1)

- [DejAIvu: Identifying and Explaining AI Art on the Web in Real-Time with Saliency Maps](https://mlsanity.com/paper/2502.08821/) (2025)

## Abstract (arXiv)

We present "DistML.js", a library designed for training and inference of machine learning models within web browsers. Not only does DistML.js facilitate model training on local devices, but it also supports distributed learning through communication with servers. Its design and define-by-run API for deep learning model construction resemble PyTorch, thereby reducing the learning curve for prototyping. Matrix computations involved in model training and inference are executed on the backend utilizing WebGL, enabling high-speed calculations. We provide a comprehensive explanation of DistML.js's design, API, and implementation, alongside practical applications including data parallelism in learning. The source code is publicly available at https://github.com/mil-tokyo/distmljs.