将智能体AI与联邦学习结合，解决无线网络中的隐私与数据异构问题。
Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications
- 通过联邦学习实现本地智能体协同训练，不交换原始数据。
- 不同联邦学习类型可增强智能体决策、规划等环节的性能。
- 在低空无线网络中验证了该方法的有效性，提升决策效率。
智能体人工智能（Agentic AI）为实现自主、自我优化的无线网络服务提供了新路径。然而，无线网络资源受限、分布广泛且数据异构，使得依赖中心化架构的现有智能体AI面临通信开销高、隐私风险大以及非独立同分布（non-IID）数据等问题。联邦学习（FL）可通过协同本地学习与参数共享，在不交换原始数据的前提下，提升智能体AI的整体闭环性能。本文提出面向无线网络的新型联邦智能体AI方法。首先总结智能体AI基础与主流联邦学习类型；其次阐述各类联邦学习如何增强智能体循环中的特定组件；进一步以基于联邦强化学习（FRL）的案例研究，验证其在低空无线网络（LAWNs）中提升智能体动作决策性能的效果；最后总结并展望未来研究方向。
原文摘要 · Abstract (English)
Agentic artificial intelligence (AI) presents a promising pathway toward realizing autonomous and self-improving wireless network services. However, resource-constrained, widely distributed, and data-heterogeneous nature of wireless networks poses significant challenges to existing agentic AI that relies on centralized architectures, leading to high communication overhead, privacy risks, and non-independent and identically distributed (non-IID) data. Federated learning (FL) has the potential to improve the overall loop of agentic AI through collaborative local learning and parameter sharing without exchanging raw data. This paper proposes new federated agentic AI approaches for wireless networks. We first summarize fundamentals of agentic AI and mainstream FL types. Then, we illustrate how each FL type can strengthen a specific component of agentic AI's loop. Moreover, we conduct a case study on using FRL to improve the performance of agentic AI's action decision in low-altitude wireless networks (LAWNs). Finally, we provide a conclusion and discuss future research directions.
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