Open Access iconOpen Access



A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting Sites

Tianming Zhang1, Zebin Chen1, Haonan Guo2, Bojun Ren1, Quanmin Xie3,*, Mengke Tian4,*, Yong Wang4

1 Department of Computer and Science, Shanghai Jiaotong University, Shanghai, 200240, China
2 Aerospace System Engineering Shanghai, Shanghai, 200240, China
3 State Key Laboratory of Precision Blasting, Jianghan University, Wuhan, 430056, China
4 Beijing Microelectronics Technology Institute, Beijing, 100076, China

* Corresponding Authors: Quanmin Xie. Email: email; Mengke Tian. Email: email

(This article belongs to this Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)

Computer Modeling in Engineering & Sciences 2024, 139(2), 2139-2154.


The data analysis of blasting sites has always been the research goal of relevant researchers. The rise of mobile blasting robots has aroused many researchers’ interest in machine learning methods for target detection in the field of blasting. Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience, which has aroused people’s interest in how to use it in the field of machine learning. In this paper, we design a distributed machine learning training application based on the AWS Lambda platform. Based on data parallelism, the data aggregation and training synchronization in Function as a Service (FaaS) are effectively realized. It also encrypts the data set, effectively reducing the risk of data leakage. We rent a cloud server and a Lambda, and then we conduct experiments to evaluate our applications. Our results indicate the effectiveness, rapidity, and economy of distributed training on FaaS.


Cite This Article

Zhang, T., Chen, Z., Guo, H., Ren, B., Xie, Q. et al. (2024). A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting Sites. CMES-Computer Modeling in Engineering & Sciences, 139(2), 2139–2154.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 80


  • 61


  • 0


Share Link