提出异步联邦双层学习算法Argus，适配6G空天地一体化网络。
Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Network
- 设计异步算法，支持动态网络中多智能体协作训练。
- 理论证明算法在非凸非光滑场景下收敛，降低通信与计算开销。
- 适合无人机等移动节点在6G网络中实现高效协同学习。
空-天-地一体化网络（SAGIN）是6G网络的核心组成部分。然而，由于缺乏基础设施且环境动态变化，传统集中式同步优化算法不适用。本文提出一种新型异步算法Argus，用于解决SAGIN中非凸、非光滑的去中心化联邦双层学习问题。该算法使联网智能体（如自主飞行器）可在时变网络中异步完成双层学习任务，避免慢速节点拖累整体训练速度。论文提供了对迭代复杂度、通信复杂度和计算复杂度的理论分析，并通过数值实验验证了其有效性。
原文摘要 · Abstract (English)
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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