Knowledge Commons of Institute of Automation，CAS
| A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks | |
| Jin Xie, San-Yang Liu, Jia-Xi Chen | |
| Source Publication | Machine Intelligence Research
 |
| ISSN | 2731-538X |
| 2022 | |
| Volume | 19Issue:1Pages:63-74 |
| Abstract | 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. |
| Keyword | Distributed learning (DL) semi-supervised learning (SSL) manifold regularization (MR) single layer feed-forward neural network (SLFNN) privacy preserving |
| DOI | 10.1007/s11633-022-1315-6 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/46650 |
| Collection | 学术期刊_Machine Intelligence Research |
| Affiliation | School 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. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| IJAC-2021-03-063.pdf（1245KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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