Comparison Distributed and Parallel Machine Learning: Evidence from Models, Principles and Application Scenarios

Abstract. Contemporarily, the demand for processing large-scale data has been rapidly increasing, prompting continuous advancements in the field of machine learning. This study examines the state of parallel and distributed machine learning at present, both of which aim to enhance computational efficiency when dealing with large datasets and demonstrate promising applications across various domains. As the scale of data escalates, traditional single-machine learning methods are becoming increasingly inadequate, which lead to the emergence of parallel and distributed machine learning. These approaches enable substantial computations to be performed more efficiently through the collaboration of multi-core CPUs or multiple computing nodes. This research conducts an in-depth analysis of the inherent challenges associated with these methods, including data transmission latency, synchronization requirements, and user privacy concerns. Ultimately, this research emphasizes the tremendous potential of both methods for future applications, a potential that is bolstered by ongoing advancements in hardware and algorithm optimization. These results provide valuable insights for practitioners in the field and offers guidance for future research directions.