The invention belongs to the technical field of
satellite communication and edge intelligent fusion, and relates to a
low earth orbit satellite collaborative
federated learning air aggregation
system and a joint scheduling method, comprising: a plurality of static ground equipment terminals, a
constellation network composed of a plurality of multi-beam
low earth orbit (LEO) satellites, a ground
data processing center, and a
data processing center. The static ground equipment terminal is in communication connection with the multi-beam low-
orbit satellite, and the satellite is in communication connection with the ground
data processing center through a gateway;
global model training is completed through a double-layer air aggregation mechanism of equipment terminal-LEO satellite-data
processing center, an optimization model is constructed, a wave
beam hopping mode, terminal transmitting power and a receiving end normalization factor are jointly optimized, a
loss function of the training model is minimized, an air aggregation error is taken as a constraint, and the overall performance of the device terminal-LEO satellite-data
processing center is improved. Adaptive scheduling is realized through deep
reinforcement learning; according to the method, the multi-device cooperative training efficiency can be remarkably improved, and efficient federal learning of large-scale distributed devices is achieved.