Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 4
1071-1074,1104

Semi-supervised federated learning model based on AutoEncoder neural network

Hou Kunchi1
Wang Nan1
Zhang Kejia1
Song Lei2
Yuan Qi3
Miao Fengjuan3
1. School of Mathematical Science, Heilongjiang University, Harbin 150080, China
2. College of Computer Science & Technology, Harbin Engineering University, Harbin 150001, China
3. College of Communication & Electronic Engineering, Qiqihar University, Qiqihar Heilongjiang 161006, China
DOI: 10.19734/j.issn.1001-3695.2021.08.0374

Abstract

Federated learning is a novel distributed machine learning approach, which provides a privacy protection way to learn a shared model without sharing each client's private data. However, the existing frameworks of federated learning only work for supervised learning wherein each client's data is labeled. Since collecting labeled data is difficult and expensive to obtain in the real world, the assumption of federated learning is not valid. To solve this problem, this paper proposed a semi-supervised federated learning model named ANN-SSFL based on an AutoEncoder neural network. The proposed model was extended from classical federated learning and allowed clients who might not have labeled data to participate the federated learning. The latent features which could be identified by the classifier were obtained by AutoEncoder neural network from unlabeled data, therefore unlabeled data could provide their data information to make their contributions. This paper conducted experiments on MNIST data sets. The experimental results show that the proposed ANN-SSFL is practical and effective. When the number of supervised clients remains unchanged, adding unsupervised clients can improve the accuracy of classical federated learning.

Foundation Support

国家自然科学基金资助项目(61872204,61802118)
黑龙江省自然基金资助项目(JQ2019F003)
黑龙江省省属本科高校基本科研业务费科研项目(135309453)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.08.0374
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 4
Section: Algorithm Research & Explore
Pages: 1071-1074,1104
Serial Number: 1001-3695(2022)04-019-1071-04

Publish History

[2021-12-08] Accepted Paper
[2022-04-05] Printed Article

Cite This Article

侯坤池, 王楠, 张可佳, 等. 基于自编码神经网络的半监督联邦学习模型 [J]. 计算机应用研究, 2022, 39 (4): 1071-1074,1104. (Hou Kunchi, Wang Nan, Zhang Kejia, et al. Semi-supervised federated learning model based on AutoEncoder neural network [J]. Application Research of Computers, 2022, 39 (4): 1071-1074,1104. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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