A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks
文献类型：期刊论文
| 作者 | Jin Xie; San-Yang Liu; Jia-Xi Chen |
| 刊名 | Machine Intelligence Research
 |
| 出版日期 | 2022 |
| 卷号 | 19期号:1页码:63-74 |
| 关键词 | Distributed learning (DL) semi-supervised learning (SSL) manifold regularization (MR) single layer feed-forward neural network (SLFNN) privacy preserving |
| ISSN号 | 2731-538X |
| DOI | 10.1007/s11633-022-1315-6 |
| 英文摘要 | 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 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55928]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | School of Mathematics and Statistics, Xidian University, Xi′an 710071, China |
| 推荐引用方式 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,et al."A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks".Machine Intelligence Research 19.1(2022):63-74. |
入库方式： OAI收割
来源：自动化研究所
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