CASIA OpenIR  > 学术期刊  > Machine Intelligence Research
A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks
Jin Xie, San-Yang Liu, Jia-Xi Chen
Source PublicationMachine Intelligence Research
ISSN2731-538X
2022
Volume19Issue:1Pages:63-74
AbstractThis 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 exchanges 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.
KeywordDistributed learning (DL) semi-supervised learning (SSL) manifold regularization (MR) single layer feed-forward neural network (SLFNN) privacy preserving
DOI10.1007/s11633-022-1315-6
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46650
Collection学术期刊_Machine Intelligence Research
AffiliationSchool of Mathematics and Statistics, Xidian University, Xi′an 710071, China
Recommended Citation
GB/T 7714
Jin Xie, San-Yang Liu, Jia-Xi Chen. A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks[J]. Machine Intelligence Research,2022,19(1):63-74.
APA Jin Xie, San-Yang Liu, Jia-Xi Chen.(2022).A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks.Machine Intelligence Research,19(1),63-74.
MLA Jin Xie, San-Yang Liu, Jia-Xi Chen."A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks".Machine Intelligence Research 19.1(2022):63-74.
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