JSDoop and TensorFlow.js: Volunteer Distributed Web Browser-Based Neural Network Training
This work addresses the challenge of leveraging idle browser resources for distributed neural network training, offering a novel approach to volunteer computing, though it is incremental as a proof-of-concept.
The authors tackled the problem of underutilized computational resources in web browsers by introducing JSDoop, a volunteer-based distributed computing library, and demonstrated its feasibility by training a recurrent neural network for text prediction with up to 32 volunteers, showing high scalability and accuracy.
In 2019, around 57\% of the population of the world has broadband access to the Internet. Moreover, there are 5.9 billion mobile broadband subscriptions, i.e., 1.3 subscriptions per user. So there is an enormous interconnected computational power held by users all around the world. Also, it is estimated that Internet users spend more than six and a half hours online every day. But in spite of being a great amount of time, those resources are idle most of the day. Therefore, taking advantage of them presents an interesting opportunity. In this study, we introduce JSDoop, a prototype implementation to profit from this opportunity. In particular, we propose a volunteer web browser-based high-performance computing library. JSdoop divides a problem into tasks and uses different queues to distribute the computation. Then, volunteers access the web page of the problem and start processing the tasks in their web browsers. We conducted a proof-of-concept using our proposal and TensorFlow.js to train a recurrent neural network that predicts text. We tested it in a computer cluster and with up to 32 volunteers. The experimental results show that training a neural network in distributed web browsers is feasible and accurate, has a high scalability, and it is an interesting area for research.