Towards a Better Distributed Framework for Learning Big Data

Abstract

This project studies how to perform machine learning tasks more efficiently and effectively in a distributed environment by exploring and solving the following problems: 1) design a general decomposition framework that allows easy scaling of the learning algorithms to a distributed environment without re-designing for parallelism, 2) handle several challenges presented in a distributed setting, such as overcoming the insufficient resources on local sites, balancing the communication and computation loading between server and clients, and learning from data without labels.

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 23, 2016
Source ID
FA23861514013

Entities

People

  • Shou-De Lin

Organizations

  • Air Force Office of Scientific Research
  • National Taiwan University
  • United States Air Force

Tags

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Distributed Systems and Data Platform Development
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks