Survey
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 6
1610-1617

Survey on federated semantic segmentation methods for distributed complex data samples

Dong Chengrong
Yao Junping
Li Xiaojun
Su Yi
Zhou Zhijie
Rocket Force University of Engineering, Xi'an 710025, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0420

Abstract

Semantic segmentation plays a crucial role in various fields such as medical image analysis and battlefield situational awareness. However, a single client often cannot provide a sufficient quantity and diversity of training data for the model. Therefore, it is necessary to train semantic segmentation models from distributed data, which exhibits complex and diverse characteristics. To prevent data privacy breaches and safeguard data security, the application of federated learning in the collaborative training of semantic segmentation models across multiple clients has become a hot research topic in the field. Building upon the definition of federated semantic segmentation, this paper conducted a comprehensive analysis around the key characteristics of data heterogeneity and label deficiency in distributed complex data samples. The study encompassed a review of issues, methods, and exemplary model instances in federated semantic segmentation, evaluating the applicability and characteristics of different methods, summarizing current application outcomes. The paper also proposed potential research opportunities to address the issues of data heterogeneity and label deficiency. The research provides insights and references for the development of federated semantic segmentation methods and related studies tailored for distributed complex data samples.

Foundation Support

国家自然科学基金资助项目(61833016,62227814)
陕西省科技创新团队项目(2022TD-24)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0420
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 6
Section: Survey
Pages: 1610-1617
Serial Number: 1001-3695(2024)06-002-1610-08

Publish History

[2024-01-26] Accepted Paper
[2024-06-05] Printed Article

Cite This Article

董成荣, 姚俊萍, 李晓军, 等. 面向分布式复杂数据样本的联邦语义分割方法综述 [J]. 计算机应用研究, 2024, 41 (6): 1610-1617. (Dong Chengrong, Yao Junping, Li Xiaojun, et al. Survey on federated semantic segmentation methods for distributed complex data samples [J]. Application Research of Computers, 2024, 41 (6): 1610-1617. )

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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