A Survey On Security and Privacy Aspects and Solutions for Federated Learning in Mobile Communication Networks

Abstract

In this study, we delve into cutting-edge solutions for security-centric, privacy-enhanced federated learning, a rapidly evolving area of research that bridges the gap between data privacy and collaborative machine learning. Our analysis offers a comprehensive comparative evaluation of existing methodologies, shedding light on the strengths and limitations of current approaches. By introducing new perspectives, we aim to push the boundaries of secure federated learning, exploring techniques that enhance data protection without compromising learning efficiency. Additionally, we highlight emerging challenges and opportunities in the field, emphasizing the importance of scalable, privacy-preserving mechanisms in decentralized systems. As federated learning continues to gain traction across various sectors such as healthcare, finance, and IoT, our study serves as a foundation for future research, identifying key areas for innovation and improvement. This forward-looking approach ensures that federated learning can continue to evolve as a trustworthy and robust solution for privacy-sensitive applications, addressing both current and future security concerns.