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A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

1,2,3,4,5,†, 1,2,†, 1,2,*, 1,2,4,5, 1,2, 1,2, 6, 7,8, 1,2 and 9
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou 310018, China
College of Electrical Engineering, Zhejiang University, Hangzhou 310058, China
School of Information and Electronic engineering, Zhejiang University of Science & Technology, Hangzhou 310023, China
Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis, Hangzhou 310018, China
Supercomputing Center of Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
Hithink RoyalFlush Information Network Co., Ltd., Hangzhou 310023, Zhejiang, China
Financial Information Engineering Technology Research Center of Zhejiang Province, Hangzhou 310023, China
Computer Science Department, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Author to whom correspondence should be addressed.
The two authors Jilin Zhang and Hangdi Tu contribute equally to this paper, and they are co-first authors.
Sensors 2017, 17(10), 2172;
Received: 19 August 2017 / Revised: 20 September 2017 / Accepted: 20 September 2017 / Published: 21 September 2017
PDF [2957 KB, uploaded 25 September 2017]


In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. View Full-Text
Keywords: disturbed machine learning; sensors; dynamic synchronous parallel strategy (DSP); parameter server (PS) disturbed machine learning; sensors; dynamic synchronous parallel strategy (DSP); parameter server (PS)

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Zhang, J.; Tu, H.; Ren, Y.; Wan, J.; Zhou, L.; Li, M.; Wang, J.; Yu, L.; Zhao, C.; Zhang, L. A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors. Sensors 2017, 17, 2172.

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