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业务，包括人工智能（AI）服务与普适数据传输。联邦学习（FL）作为一种革命性学习方法，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同一无线资源上并发上传模型，从而显著降低FL的通信开销。本文研究了移动边缘网络中空中计算FL与传统信息传输（IT）的共存问题。我们提出了一种共存的联邦学习与信息传输（CFLIT）通信框架，其中FL与IT设备在OFDM系统中共享无线频谱。在此框架下，我们以最大化IT数据传输速率同时保证给定FL收敛性能为目标，优化长期无线资源分配方案。制约共存系统频谱效率的关键挑战在于：为实现FL模型聚合，服务器与边缘设备间频繁通信产生巨大开销。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计算FL收敛性能的影响，揭示了存在一个最优计算-通信比，可使空中计算FL在给定误差容限内收敛所需的无线资源量最小化。基于该分析，我们提出一种低复杂度的在线算法，联合优化FL设备与IT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网络中FL与IT设备共存场景下的优越性能。