Federated learning for secure and efficient vehicular communications in open RAN

This paper presents a comprehensive exploration of federated learning applied to vehicular communications within the context of Open RAN. Through an in-depth review of existing literature and analysis of fundamental concepts, critical challenges are identified within the current methodologies employed in this sphere. A novel framework is proposed to address these shortcomings, fundamentally based on federated learning principles. This framework aims to enhance data security by decentralizing data processing and implementing encryption protocols, and to improve communication efficiency through optimized resource allocation and intelligent scheduling within the flexible Open RAN architecture. The paper further provides a rigorous justification of the proposed solution, highlighting its potential impact and the improvements it could bring to vehicular communications. Ultimately, this study offers a roadmap for future research in applying federated learning for more secure and efficient vehicular communications in Open RAN, opening new avenues for exploration in this interdisciplinary domain.