Blockchain-Based Federated Learning for Data Privacy and Security

This research delves into the application of Blockchain-Based Federated Learning (BBFL) to enhance data privacy and security. By exploring innovative techniques within federated learning, this study aims to identify potential obstacles, assess existing regulations, and propose practical solutions. Employing a mixed-methods approach encompassing case studies, surveys, and in-depth interviews, the research seeks to provide comprehensive insights into the implementation of BBFL. The collaborative efforts of stakeholders, alignment of policies with privacy-centric principles, and the secure processing of data through BBFL emerge as pivotal components for achieving robust data privacy and security objectives. The study contributes actionable recommendations, offering valuable insights for practitioners, industry stakeholders, and policymakers to advance the discourse on fortified data privacy and security in federated learning.