TY  - EJOU
AU  - Zhang, Tianming 
AU  - Chen, Zebin 
AU  - Guo, Haonan 
AU  - Ren, Bojun 
AU  - Xie, Quanmin 
AU  - Tian, Mengke 
AU  - Wang, Yong 

TI  - A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting Sites
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 139
IS  - 2
SN  - 1526-1506

AB  - 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.
KW  - Serverless computing; object detection; blasting

DO  - 10.32604/cmes.2023.043822