Special Topics in Federated Learning
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
706-712

Federated semantic segmentation algorithm under large scale differential point cloud data

Lin Jiabin1,2
Zhang Jianfeng2
Shao Dongheng2
Guo Jielong2
Yang Jing3
Wei Xian2
1. College of Mechanical & Electrical Engineering, Fujian Agriculture & Forestry University, Fuzhou 350100, China
2. Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China
3. Longhe Intelligent Equipment Manufacturing Co. , Ltd. , Longyan Fujian 364101, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0320

Abstract

The storage of massive point cloud data has great significance to the real-time 3D collaborative perception of autonomous driving. However, due to the requirements of data security and confidentiality, some data owners are unwilling to share their private point cloud data, which limits the improvement of model training accuracy. Federated learning is a computing paradigm that focuses on data privacy and security. This paper proposed a novel approach based on federated learning to address the challenge of large-scale point cloud semantic segmentation in collaborative vehicle perception scenarios. It integrated position encoding with inter-point angle information and geometric diffraction of neighboring points to enhance the feature extraction capability of the model. Finally, it dynamically adjusted the aggregation weights of the global model according to the generation quality of the local model to improve the ability to maintain the local geometric structure of the data. This paper applied the proposed method on three datasets, such as SemanticKITTI, SemanticPOSS and Toronto3D. The results show that the proposed approach significantly outperforms the single training data and the FedAvg-based method, and fully exploits the value of the point cloud data while taking into account the privacy sensitivity of each party's data.

Foundation Support

福建省科技计划资助项目(2022T3053)
泉州市科技资助项目(2021C065L)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0320
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: Special Topics in Federated Learning
Pages: 706-712
Serial Number: 1001-3695(2024)03-010-0706-07

Publish History

[2023-11-17] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

林佳斌, 张剑锋, 邵东恒, 等. 大规模差异化点云数据下的联邦语义分割算法 [J]. 计算机应用研究, 2024, 41 (3): 706-712. (Lin Jiabin, Zhang Jianfeng, Shao Dongheng, et al. Federated semantic segmentation algorithm under large scale differential point cloud data [J]. Application Research of Computers, 2024, 41 (3): 706-712. )

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  • 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.

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