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

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 ofmachine learning. In this paper, we design a distributedmachine 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. [ABSTRACT FROM AUTHOR]