Analysis of Optimizing Distributed Machine Learning Techniques for Large-Scale Data Applications

This study investigates the optimization and application of Distributed Machine Learning (DML) techniques, which are essential for addressing the computational challenges posed by large-scale datasets in modern applications such as autonomous driving and natural language processing. The primary objective is to evaluate the performance and characteristics of key DML methods, including MapReduce, Parallelized Stochastic Gradient Descent (PSGD), and Federated Learning (FL). A comprehensive analytical framework is employed to explore the underlying mechanisms of each method. For MapReduce, the study examines data partitioning, mapping, shuffling, and reducing processes to assess its efficiency in managing large datasets. In PSGD, the research analyzes the methods of data allocation, local gradient computation, and parameter aggregation to highlight its parallelization benefits and identify potential bottlenecks. FL focuses on its privacy-preserving model training process, which includes client-server communication, local updates, and global aggregation. Experimental results show that while MapReduce offers fault tolerance, PSGD accelerates training on large datasets, and FL ensures data privacy. However, limitations such as communication overhead and convergence issues remain, underscoring the trade-offs between efficiency, scalability, and privacy in DML applications.