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Special Topics in Federated Learning
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
713-720

Effective method to solve problem of data heterogeneity in federated learning

Zhang Hongyan1
Zhang Yu1
Cao Canming2
1. College of Information Science & Technology, Zhengzhou Normal University, Zhengzhou 450044, China
2. School of Computer Science & Technology, Tiangong University, Tianjin 300387, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0296

Abstract

Federated learning is a framework for obtaining machine learning models without centralized data training, reducing the risk of privacy leakage while also obtaining optimized training models locally. However, the identity, behavior, environment, etc. between nodes are different, resulting in unbalanced data distribution, which may cause a large deviation in the performance of the model on different devices, resulting in data heterogeneity. Aiming at the above problems, this paper proposed a federated learning algorithm for data sharing clustering based on node optimization method that applied clustering and data sharing to federated learning system at the same time, which could effectively reduce the impact of data heterogeneity on federated learning and accelerate the convergence of local models. At the same time, it designed method to assess the convergence of the global shared model to determine the timing of node clustering nodes. Finally, this paper used the EMNIST and CIFAR-10 datasets for experiments and performance analysis to compare the effects of the size of the shared scale on the convergence speed and accuracy of each node, and to compare the accuracy of clustering and data sharing before and after the application of federated learning. Experimental results show that the convergence speed and accuracy of each node are improved when data sharing is introduced, and the accuracy is increased by about 10%~15% when clustering and data sharing are introduced into federated learning training at the same time, indicating that this method has a good effect on the heterogeneous problem of federated learning data.

Foundation Support

国家自然科学基金资助项目(61972456,62172298)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0296
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: Special Topics in Federated Learning
Pages: 713-720
Serial Number: 1001-3695(2024)03-011-0713-08

Publish History

[2023-09-06] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

张红艳, 张玉, 曹灿明. 一种解决数据异构问题的联邦学习方法 [J]. 计算机应用研究, 2024, 41 (3): 713-720. (Zhang Hongyan, Zhang Yu, Cao Canming. Effective method to solve problem of data heterogeneity in federated learning [J]. Application Research of Computers, 2024, 41 (3): 713-720. )

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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