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The space-air-ground integrated network (SAGIN), one of the key technologiesfor next-generation mobile communication systems, can facilitate datatransmission for users all over the world, especially in some remote areaswhere vast amounts of informative data are collected by Internet of remotethings (IoRT) devices to support various data-driven artificial intelligence(AI) services. However, training AI models centrally with the assistance ofSAGIN faces the challenges of highly constrained network topology, inefficientdata transmission, and privacy issues. To tackle these challenges, we firstpropose a novel topology-aware federated learning framework for the SAGIN,namely Olive Branch Learning (OBL). Specifically, the IoRT devices in theground layer leverage their private data to perform model training locally,while the air nodes in the air layer and the ring-structured low earth orbit(LEO) satellite constellation in the space layer are in charge of modelaggregation (synchronization) at different scales.To further enhancecommunication efficiency and inference performance of OBL, an efficientCommunication and Non-IID-aware Air node-Satellite Assignment (CNASA) algorithmis designed by taking the data class distribution of the air nodes as well astheir geographic locations into account. Furthermore, we extend our OBLframework and CNASA algorithm to adapt to more complex multi-orbit satellitenetworks. We analyze the convergence of our OBL framework and conclude that theCNASA algorithm contributes to the fast convergence of the global model.Extensive experiments based on realistic datasets corroborate the superiorperformance of our algorithm over the benchmark policies.

Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network