The experiment of federated learning algorithm

Abstract

As technology advances, concerns regarding data privacy and security have become prominent challenges in machine learning applications. Nevertheless, the introduction of federated learning technology has effectively tackled this concern by concurrently enhancing model performance and preserving data privacy, thereby presenting a more secure and efficient solution within our digital realm. Utilizing discussions about the background of federated learning technology, coupled with pertinent algorithmic procedures and logic, this paper proficiently implements the FedMA algorithm. Additionally, the study performs a comparative analysis of the accuracy and efficiency of the FedMA, FedAvg, FedDyn, and MOON algorithms utilizing the Fashion-MNIST dataset. Moreover, the investigation not only optimizes parameter tuning for the MOON algorithm but also extends experiments to the CIFAR-10 and AGNews datasets, thereby providing additional comparisons of performance and strengths among various federated learning algorithms. In conclusion, the paper provides a comprehensive summary and outlines potential avenues for future research. These insights enhance the comprehension of federated learning and offer valuable guidance to advance and refine its practical applications.