![[2602.15212] Secure and Energy-Efficient Wireless Agentic AI Networks](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.15212] Secure and Energy-Efficient Wireless Agentic AI Networks
Summary
This paper presents a secure and energy-efficient wireless AI network that utilizes a supervisor AI agent to optimize reasoning tasks while ensuring data confidentiality and minimizing energy consumption.
Why It Matters
As AI networks become increasingly integral to various applications, ensuring their security and energy efficiency is crucial. This research addresses these challenges, proposing innovative solutions that could enhance the performance and sustainability of AI systems in real-world scenarios.
Key Takeaways
- Introduces a novel wireless AI network with a supervisor agent for task optimization.
- Proposes two resource allocation schemes that significantly reduce energy consumption.
- Demonstrates practical applications and successful validation using public benchmarks.
Computer Science > Artificial Intelligence arXiv:2602.15212 (cs) [Submitted on 16 Feb 2026] Title:Secure and Energy-Efficient Wireless Agentic AI Networks Authors:Yuanyan Song, Kezhi Wang, Xinmian Xu View a PDF of the paper titled Secure and Energy-Efficient Wireless Agentic AI Networks, by Yuanyan Song and 2 other authors View PDF HTML (experimental) Abstract:In this paper, we introduce a secure wireless agentic AI network comprising one supervisor AI agent and multiple other AI agents to provision quality of service (QoS) for users' reasoning tasks while ensuring confidentiality of private knowledge and reasoning outcomes. Specifically, the supervisor AI agent can dynamically assign other AI agents to participate in cooperative reasoning, while the unselected AI agents act as friendly jammers to degrade the eavesdropper's interception performance. To extend the service duration of AI agents, an energy minimization problem is formulated that jointly optimizes AI agent selection, base station (BS) beamforming, and AI agent transmission power, subject to latency and reasoning accuracy constraints. To address the formulated problem, we propose two resource allocation schemes, ASC and LAW, which first decompose it into three sub-problems. Specifically, ASC optimizes each sub-problem iteratively using the proposed alternating direction method of multipliers (ADMM)-based algorithm, semi-definite relaxation (SDR), and successive convex approximation (SCA), while LAW tackles each...
