The space-air-ground integrated network (SAGIN) has recently emerged as a core element in the 6G networks. However, traditional centralized and synchronous optimization algorithms are unsuitable for SAGIN due to infrastructureless and time-varying environments. This paper aims to develop a novel Asynchronous algorithm a.k.a. Argus for tackling non-convex and non-smooth decentralized federated bilevel learning over SAGIN. The proposed algorithm allows networked agents (e.g. autonomous aerial vehicles) to tackle bilevel learning problems in time-varying networks asynchronously, thereby averting stragglers from impeding the overall training speed. We provide a theoretical analysis of the iteration complexity, communication complexity, and computational complexity of Argus. Its effectiveness is further demonstrated through numerical experiments.
@article{arxiv.2505.09106,
title = {Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Network},
author = {Ya Liu and Kai Yang and Yu Zhu and Keying Yang and Haibo Zhao},
journal= {arXiv preprint arXiv:2505.09106},
year = {2025}
}