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:未来的无线网络预计支持包括人工智能(AI)服务和普遍数据传输在内的多样化移动服务。联邦学习(FL)作为一种革命性的学习方法,使得分布式移动边缘设备间协同AI模型训练成为可能。通过利用多址信道的叠加特性,无线计算允许大量设备可以在相同的无线资源上同时上传模型,从而显著降低了FL的通信成本。在本文中,我们研究了在移动边缘网络中实现联合过空中FL和传统信息传输(IT)的共存问题。我们提出了一个共存的联邦学习和信息传输(CFLIT)通信框架,其中FL和IT设备在OFDM系统中共享无线频谱。在这个框架下,我们旨在通过优化长期无线资源分配来实现最大化IT数据速率和保证给定FL收敛性能。限制共存系统频谱效率的一个重要挑战是FL模型聚合的频繁通信在服务端和边缘设备之间产生的大量开销。为了解决这个挑战,我们对在无线信道上完成FL的收敛所需的计算和通信比进行了严格的分析。分析揭示了存在一个最优的计算和通信比,它最小化了过空中FL达到给定误差容限所需的无线资源量。基于此分析,我们提出了一个低复杂度的在线算法,来联合优化FL设备和IT设备的无线资源分配。广泛的数值模拟验证了该设计在无线蜂窝系统中实现FL和IT设备共存的卓越性能。