Federated Learning for Intelligent Transmission with Space-Air-Ground Integrated Network toward 6G

Abstract

The future intelligent devices requires ultra-low communication delay and high QoS requirement for the following beyond-5G network. Space-air-ground Integrated Network (SAGIN) integrated with satellite networks in space, aerial networks, and terrestrial networks is an advanced network framework to expand the networking overage and improve connectivity for intelligent applications. However, due to the heterogeneous structure and high dynamic of SAGIN, the resource management and transmission strategy should be carefully designed to adjust to the unbalanced resources and varying environments. Federated learning is an innovative distributed learning method to intelligently manage the resource scheduling problem in SAGIN with security and guarantee of user privacy. In this paper, we introduce the advantages and potential usage directions of using federated learning in SAGIN in terms of different optimization objectives. To better illustrate the potential deployment of federated learning in SAGIN, we further provide a case study of a federated reinforcement learning-based traffic offloading approach in SAGIN.