NDLI: Learning better while sending less: Communication-efficient online semi-supervised learning in client-server settings

Content Provider	IEEE Xplore Digital Library
Author	Han Xiao Shou-De Lin Mi-Yen Yeh Gibbons, P.B. Eckert, C.
Copyright Year	2015
Description	Author affiliation: Intel Labs., Pittsburgh, PA, USA (Gibbons, P.B.) \|\| Acad. Sinica, Taipei, Taiwan (Mi-Yen Yeh) \|\| Tech. Univ. Munchen, Garching, Germany (Han Xiao; Eckert, C.) \|\| Nat. Taiwan Univ., Taipei, Taiwan (Shou-De Lin)
Abstract	We consider a novel distributed learning problem: A server receives potentially unlimited data from clients in a sequential manner, but only a small initial fraction of these data are labeled. Because communication bandwidth is expensive, each client is limited to sending the server only a small (high-priority) fraction of the unlabeled data it generates, and the server is limited in the amount of prioritization hints it sends back to the client. The goal is for the server to learn a good model of all the client data from the labeled and unlabeled data it receives. This setting is frequently encountered in real-world applications and has the characteristics of online, semi-supervised, and active learning. However, previous approaches are not designed for the client-server setting and do not hold the promise of reducing communication costs. We present a novel framework for solving this learning problem in an effective and communication-efficient manner. On the server side, our solution combines two diverse learners working collaboratively, yet in distinct roles, on the partially labeled data stream. A compact, online graph-based semi-supervised learner is used to predict labels for the unlabeled data arriving from the clients. Samples from this model are used as ongoing training for a linear classifier. On the client side, our solution prioritizes data based on an active-learning metric that favors instances that are close to the classifier's decision hyperplane and yet far from each other. To reduce communication, the server sends the classifier's weight-vector to the client only periodically. Experimental results on real-world data sets show that this particular combination of techniques outperforms other approaches, and in particular, often outperforms (communication expensive) approaches that send all the data to the server.
Sponsorship	IEEE Comput. Intell. Soc.
Starting Page	1
Ending Page	10
File Size	635178
Page Count	10
File Format	PDF
ISBN	9781467382724
DOI	10.1109/DSAA.2015.7344833
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-19
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semi-supervised learning Distributed databases Bandwidth Semisupervised learning Cameras Data models Big data Distributed system Servers Labeling Online learning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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