Federated learning raises issues with data privacy, security, and transparency in the training process that have prevented its widespread implementation. However, it enables cooperative machine learning across distant devices without sharing raw data. Researchers have looked into incorporating blockchain technology into federated learning to address these problems because of how its immutable and decentralized structure can improve data security, privacy, and accountability. Many studies have looked into how blockchain might improve data security in a variety of businesses, but federated learning specifically is still a relatively new and developing field of study. Building on previous research, this study looks at technological methods for combining blockchain and federated learning, suggests fresh ways, and uses simulations to assess each one’s efficacy in order to produce useful advice for real-world implementation.