Improve Transmission Fault Tolerance and Speed for Distributed Machine Learning

Abstract. As a matter of fact, with the advent of the big data era, traditional stand-alone machine learning methods are facing computational as well as storage bottlenecks. On this basis, distributed machine learning has become a popular research direction. Withs this in mind, this research explores the basic principles of distributed machine learning and its application on large-scale datasets. According to the analysis, it is shown that the use of distributed architecture can effectively shorten the model training time and improve the scalability of the system. By comparing different distributed algorithms, it is found that the architecture based on parameter servers has obvious advantages in dealing with heterogeneous data. In addition, the experimental results show that a reasonable data partitioning and parallel computing strategy can significantly improve the training efficiency and the final performance of the model at the same time. The significance of this study is that it provides a theoretical foundation and practical guidance for distributed machine learning, which helps to promote its wide application in industry.