Distributed learning of deep neural network over multiple agents
Name
1810.06060.pdf
Description
Accepted version
Size
677.91 KB
Format
Adobe PDF
Checksum (MD5)
27998e0f40697289dcee61049547d314
Author(s)
Gupta, Otkrist
Gupta, Otkrist
Raskar, Ramesh
Date Issued
August 2018
Journal
Journal of Network and Computer Applications
Publisher
Elsevier BV
Citation
Gupta, Otkrist and Ramesh Raskar. "Distributed learning of deep neural network over multiple agents." Journal of Network and Computer Applications 116 (August 2018): 1-8 © 2018 Elsevier Ltd
Version
Author's final manuscript
Abstract
In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.jnca.2018.05.003
