Asynchronous broadcast-based decentralized learning in sensor networks

In this paper, we study the problem of decentralized learning in sensor networks in which local learners estimate and reach consensus to the quantity of interest inferred globally while communicating only with their immediate neighbours. The main challenge lies in reducing the communication cost in...

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Veröffentlicht in:International journal of parallel, emergent and distributed systems Jg. 33; H. 6; S. 589 - 607
Hauptverfasser: Zhao, Liang, Song, Wen-Zhan, Ye, Xiaojing, Gu, Yujie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 02.11.2018
Taylor & Francis Ltd
Schlagworte:
Asynchronous algorithm
big data
Communication
Data exchange
decentralized learning
Machine learning
Mathematical models
Networks
Production planning
sensor network
Sensors
ISSN:1744-5760, 1744-5779
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Abstract In this paper, we study the problem of decentralized learning in sensor networks in which local learners estimate and reach consensus to the quantity of interest inferred globally while communicating only with their immediate neighbours. The main challenge lies in reducing the communication cost in the network, which involves inter-node synchronisation and data exchange. To address this issue, a novel asynchronous broadcast-based decentralized learning algorithm is proposed. Furthermore, we prove that the iterates generated by the developed decentralized method converge to a consensual optimal solution (model). Numerical results demonstrate that it is a promising approach for decentralized learning in sensor networks. The execution model on a decentralized sensor network and the workflow of asynchronous computing.
AbstractList In this paper, we study the problem of decentralized learning in sensor networks in which local learners estimate and reach consensus to the quantity of interest inferred globally while communicating only with their immediate neighbours. The main challenge lies in reducing the communication cost in the network, which involves inter-node synchronisation and data exchange. To address this issue, a novel asynchronous broadcast-based decentralized learning algorithm is proposed. Furthermore, we prove that the iterates generated by the developed decentralized method converge to a consensual optimal solution (model). Numerical results demonstrate that it is a promising approach for decentralized learning in sensor networks.
In this paper, we study the problem of decentralized learning in sensor networks in which local learners estimate and reach consensus to the quantity of interest inferred globally while communicating only with their immediate neighbours. The main challenge lies in reducing the communication cost in the network, which involves inter-node synchronisation and data exchange. To address this issue, a novel asynchronous broadcast-based decentralized learning algorithm is proposed. Furthermore, we prove that the iterates generated by the developed decentralized method converge to a consensual optimal solution (model). Numerical results demonstrate that it is a promising approach for decentralized learning in sensor networks. The execution model on a decentralized sensor network and the workflow of asynchronous computing.
Author Song, Wen-Zhan
Ye, Xiaojing
Gu, Yujie
Zhao, Liang
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Cites_doi 10.1016/j.jvolgeores.2007.06.008
10.1109/ICASSP.2012.6288575
10.1109/JSTSP.2011.2120593
10.1109/CDC.2013.6760448
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10.1111/j.1365-246X.1987.tb00728.x
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10.1109/GlobalSIP.2013.6736937
10.1145/2107736.2107740
10.1109/ISWPC.2008.4556312
10.1109/TSP.2009.2016247
10.1109/SFCS.2003.1238221
10.1109/BigData.2015.7363839
10.1109/CDC.2013.6760975
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SubjectTerms Asynchronous algorithm
big data
Communication
Data exchange
decentralized learning
Machine learning
Mathematical models
Networks
Production planning
sensor network
Sensors
Title Asynchronous broadcast-based decentralized learning in sensor networks
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