Jin Xie, San-Yang Liu, Jia-Xi Chen. A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks. International Journal of Automation and Computing.
Citation: Jin Xie, San-Yang Liu, Jia-Xi Chen. A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks. International Journal of Automation and Computing.

A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks

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  • Author Bio:

    Jin Xie received the B. Sc. degree in mathematics from North China Electric Power University, China in 2013, and received the Ph. D. degree in mathematics from Xidian University, China in 2020. He is currently a lecturer with School of Mathematics and Statistics, Xidian University, China.His research interests include machine learning, neural networks, semi-supervised learning, and distributed learning

    San-Yang Liu received the B. Sc. or B. Eng degree from Shaanxi Normal University in 1982, M. Sc. or degree from Xidian University, China in 1984, and received the Ph. D. degree from Xi′an Jiao Tong University, China in 1989. Currently, he is the Director of Institute of Industrial and Applied Mathematics, Director of the Center for Mathematics and Cross Science of Xidian University, China.His research interests include optimization methods and their applications, nonlinear analysis, and system modeling

    Jia-Xi Chen received the M. Sc. and Ph. D. degrees in applied mathematics from Xidian University, China in 2018 and 2020, respectively. He is currently a lecturer at Department of Applied Mathem- atics, Xidian University, China.His research interests include adaptive control, multi-agent systems and Takagi-Sugeno (T-S) fuzzy systems

  • Received Date: 2021-03-15
  • Accepted Date: 2021-08-19
  • This paper aims to propose a framework for manifold regularization (MR) based distributed semi-supervised learning (DSSL) using single layer feed-forward neural network (SLFNN). The proposed framework, denoted as DSSL-SLFNN is based on the SLFNN, MR framework, and distributed optimization strategy. Then, a series of algorithms are derived to solve DSSL problems. In DSSL problems, data consisting of labeled and unlabeled samples are distributed over a communication network, where each node has only access to its own data and can only communicate with its neighbors. In some scenarios, DSSL problems cannot be solved by centralized algorithms. According to the DSSL-SLFNN framework, each node over the communication network the initial parameters of the SLFNN with the same basis functions for semi-supervised learning (SSL). All nodes calculate the global optimal coefficients of the SLFNN by using distributed datasets and local updates. During the learning process, each node only exchanges local coefficients with its neighbors rather than raw data. It means that DSSL-SLFNN based algorithms work in a fully distributed fashion and are privacy preserving methods. Finally, several simulations are presented to show the efficiency of the proposed framework and the derived algorithms.


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