Journal
IEEE NETWORK
Volume 30, Issue 3, Pages 22-29Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/mnet.2016.7474340
Keywords
-
Categories
Funding
- A*STAR Computational Resource Centre
- National Research Foundation of Korea (NRF) - Korean government (MSIP) [2014R1A5A1011478]
- Singapore MOE [RG18/13, RG33/12]
- MOE [MOE2014-T2-2-015 ARC 4/15]
- U.S. National Science Foundation [US NSF ECCS-1547201, CCF-1456921, CNS-1443917, ECCS-1405121, NSFC61428101]
Ask authors/readers for more resources
The proliferation of mobile devices, such as smartphones and Internet of Things gadgets, has resulted in the recent mobile big data era. Collecting mobile big data is unprofitable unless suitable analytics and learning methods are utilized to extract meaningful information and hidden patterns from data. This article presents an overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark. Specifically, distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall mobile, and a master deep model is then built by averaging the parameters of all partial models. This Spark-based framework speeds up the learning of deep models consisting of many hidden layers and millions of parameters. We use a context-aware activity recognition application with a real-world dataset containing millions of samples to validate our framework and assess its speedup effectiveness.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available