Consensus Learning for Distributed Fuzzy Neural Network in Big Data Environment

Ye SHI, Chin-Teng LIN, Yu-Cheng CHANG, Weiping DING, Yuhui SHI, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

11 Citations (Scopus)


Uncertainty and distributed nature inherently exist in big data environment. Distributed fuzzy neural network (D-FNN) that not only employs fuzzy logics to alleviate the uncertainty problem but also deal with data in a distributed manner, is effective and crucial for big data. Existing D-FNNs always avoided consensus for their antecedent layer due to computational difficulty. Hence such D-FNNs are not really distributed since a single model can not be agreed by multiple agents. This article proposes a true D-FNN model to handle the uncertainty and distributed challenges in the big data environment. The proposed D-FNN model considers consensus for both the antecedent and consequent layers. A novel consensus learning, which involves a distributed structure learning and a distributed parameter learning, is proposed to handle the D-FNN model. The proposed consensus learning algorithm is built on the well-known alternating direction method of multipliers, which does not exchange local data among agents. The major contribution of this paper is to propose the true D-FNN model for the big data and the novel consensus learning algorithm for this D-FNN model. Simulation results on popular datasets demonstrate the superiority and effectiveness of the proposed D-FNN model and consensus learning algorithm. © 2017 IEEE.
Original languageEnglish
Article number9112660
Pages (from-to)29-41
Number of pages13
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number1
Early online date9 Jun 2020
Publication statusPublished - Feb 2021
Externally publishedYes

Bibliographical note

This work was supported in part by the Australian Research Council under discovery under Grants DP180100670 and DP180100656, in part by the National Natural Science Foundation of China under Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and in part by the Qing Lan Project of Jiangsu Province. (Ye Shi and Chin-Teng Lin contributed equally to this work.)


  • Big data
  • consensus learning
  • distributed fuzzy neural network (D-FNN)
  • distributed parameter learning
  • distributed structure learning


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