The low-altitude economy (LAE) is a rapidly emerging paradigm that builds a service-centric economic ecosystem through large-scale and sustainable uncrewed aerial vehicle (UAV)-enabled service provisioning, reflecting the transition of the 6G era from technological advancement toward commercial deployment. The significant market potential of LAE attracts an increasing number of service providers (SPs), resulting in intensified competition in service deployment. In this paper, we study a realistic LAE scenario in which multiple SPs dynamically deploy UAVs to deliver multiple services to user hotspots, aiming to jointly optimize communication and computation resource allocation. To resolve deployment competition among SPs, an authenticity-guaranteed auction mechanism is designed, and game-theoretic analysis is conducted to establish the solvability of the proposed resource allocation problem. Furthermore, a resilient federated reinforcement learning (FRL)-based solution is developed with strong fault tolerance, effectively countering transmission errors and malicious competition while facilitating potential cooperation among self-interested SPs. Simulation results demonstrate that the proposed approach significantly improves service performance and robustness compared with baseline methods, providing a practical and scalable solution for competitive LAE service deployment.

翻译：低空经济（LAE）是一种快速兴起的范式，它通过大规模、可持续的无人机（UAV）赋能服务供给，构建了一个以服务为中心的经济生态系统，反映了6G时代从技术进步向商业部署的转变。LAE巨大的市场潜力吸引了越来越多的服务提供商（SPs），导致服务部署竞争加剧。本文研究了一个现实的LAE场景，其中多个SPs动态部署无人机，向用户热点提供多种服务，旨在联合优化通信和计算资源分配。为解决SPs之间的部署竞争，设计了一种真实性保障的拍卖机制，并进行了博弈论分析，以证明所提资源分配问题的可解性。此外，开发了一种基于弹性联邦强化学习（FRL）的解决方案，该方案具有强大的容错能力，能有效应对传输错误和恶意竞争，同时促进自利SPs之间的潜在合作。仿真结果表明，与基线方法相比，所提方法显著提高了服务性能和鲁棒性，为竞争性LAE服务部署提供了一个实用且可扩展的解决方案。