Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications

2026-03-02

AI Summary

This work addresses the challenges posed by resource constraints, data heterogeneity, and privacy requirements in wireless networks, which render traditional centralized AI agents inefficient due to high communication overhead and non-IID data distributions. To overcome these limitations, we propose the first federated agent-based AI framework tailored for wireless networks, integrating federated learning with a closed-loop agent architecture. The framework enables collaborative enhancement of perception, decision-making, and self-optimization capabilities through local coordination and parameter sharing—without transmitting raw data. Specifically, we design a federated reinforcement learning–based action decision mechanism and validate its efficacy in a low-altitude wireless network scenario. Experimental results demonstrate that the proposed approach significantly improves the decision-making performance of distributed agents, offering a novel paradigm for building privacy-preserving, autonomously evolving wireless networks.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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.

Problem

• Agentic AI
• Federated Learning
• Wireless Networks
• non-IID data
• Privacy

Innovation

• Federated Agentic AI
• Federated Learning
• Wireless Networks
• Non-IID Data
• Low-Altitude Wireless Networks