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With the increase of intelligent devices leading to increasing demand fortraffic, traffic offloading has become a challenging problem. Thespace-air-ground integrated network (SAGIN) is a superior network architectureto solve this problem. The existing research on SAGIN traffic offloading onlyconsiders the single-layer satellite network in the space network. To furtherexpand the resource pool of traffic offloading in SAGIN, we extend thesingle-layer satellite network into a double-layer satellite network composedof low-orbit satellites (LEO) and high-orbit satellites (GEO). And re-model afour-layer SAGIN architecture consisting of the ground network, the airnetwork, LEO and GEO. Furthermore, we propose a novel Federated SoftActor-Critic (FeSAC) traffic offloading method with positive environmentalexploration to accommodate this dynamic and complex four-layer SAGINarchitecture. The FeSAC method uses federated learning to train trafficoffloading nodes and then aggregate the training results to obtain the besttraffic offloading strategy. The simulation results show that under thefour-layer SAGIN, our proposed method can better adapt to the networkenvironment changes by nodes mobility and is better than the existing trafficoffloading methods in throughput, packet loss, and transmission delay.

FeSAC: Federated Learning-Based Soft Actor-Critic Traffic Offloading in Space-Air-Ground Integrated Network