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arXiv · 2505.09106

Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Network

Ya Liu·Kai Yang·Yu Zhu·Keying Yang·Haibo Zhao
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Abstract

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.

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Ya Liu, Kai Yang, Yu Zhu, Keying Yang, Haibo Zhao. 2025-05-14. Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Network. https://arxiv.org/abs/2505.09106

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