Comparative Analysis of Distributed and Parallel Machine Learning: Principles, Models, and Applications

Abstract. Contemporarily, the explosive growth of technology and the growth of the complexity of machine learning models facilitated the advancement of parallel and distributed machine learning. This study compared and analyzed the two paradigms from three perspectives: principles, models, and applications. This research analyzed the basic principle and some important models of parallel and distributed machine learning, mainly focusing on their operating mechanism, scalability, and performance metrics. The analysis includes how these methods can help to deal with large datasets and complex models. According to the evaluations, distributed machine learning is better in managing massive data and achieving model convergence, while parallel machine learning performs well in processing complicated models quickly. These results provided opinions on each methods advantages and disadvantages and suggestions on how to choose between the two methods in different situations. This research also stressed the importance of understanding these differences in advancing machine learning and promoting innovation in data-intensive fields.