Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the coexistence of over-the-air FL and traditional information transfer (IT) in a mobile edge network. We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an OFDM system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.

翻译：未来无线网络需支持多样化移动服务，包括人工智能（AI）服务与普适数据传输。联邦学习作为一种革命性学习范式，能够实现分布式移动边缘设备间的协作式AI模型训练。通过利用多址信道的叠加特性，空中计算技术允许海量设备在相同无线资源上同步进行模型上传，从而显著降低联邦学习的通信开销。本文研究了移动边缘网络中空中联邦学习与传统信息传输的共存问题。我们提出一种共存联邦学习与信息传输（CFLIT）通信框架，其中联邦学习设备与信息传输设备在OFDM系统中共享无线频谱。在该框架下，我们通过优化长期无线资源分配，以最大化信息传输速率并保证给定的联邦学习收敛性能。制约共存系统频谱效率的关键挑战在于联邦学习模型聚合过程中服务器与边缘设备间频繁通信产生的高额开销。为应对该挑战，我们严格分析了计算通信比对无线衰落信道中空中联邦学习收敛性的影响。分析揭示了存在最优计算通信比，使得空中联邦学习收敛到给定误差容限所需的无线资源最小化。基于分析结果，我们提出一种低复杂度在线算法，联合优化联邦学习设备与信息传输设备的无线资源分配。大量数值仿真验证了所提方案在无线蜂窝系统中实现联邦学习设备与信息传输设备共存时的优越性能。