When communication networks meet federated learning for intelligence interconnecting: A comprehensive survey and future perspective
Sha Zongxuan, Ru Huo, Sun Chuang, Shuo Wang, Huang Tao, Fei Richard Yu · China Communications · 2025
With the rapid development of network technologies, alarge number of deployed edge devices and information systems generate massive amounts of data which provide good support for the advancement of data-driven intelligent models. However, these data often contain sensitive information of users. Federated learning (FL), as a privacy preservation machine learning setting, allows users to obtain a well-trained model without sending the privacy-sensitive local data to the central server. Despite the promising prospect of FL, several significant research challenges need to be addressed before widespread deployment, including network resource allocation, model security, model convergence, etc. In this paper, we first provide a brief survey on some of these works that have been done on FL and discuss the motivations of the Communication Networks (CNs) and FL to mutually enable each other. We analyze the support of network technologies for FL, which requires frequent communication and emphasizes security, as well as the studies on the intelligence of many network scenarios and the improvement of network performance and security by the methods based on FL. At last, some challenges and broader perspectives are explored.