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:预计未来无线网络将支持多种移动服务,包括人工智能(AI)服务和移动边缘网络中传统的信息传输。作为一种革命性学习方法,联邦学习(FL)能够使分布式移动边缘设备之间合作的AI模式培训成为合作性AI模式。通过利用多个接入频道的叠加特性,超空计算可以使大型设备在同一无线电资源中同时上传模型,从而大大降低FL的通信成本。在本文件中,我们研究了在移动边缘网络中跨空FL和传统信息传输(IT)的共存情况。我们提议了一个同时存在的优化学习和信息传输(CFLIT)通信框架,使FL和IT设备在分布式移动边缘系统中共享无线频频频频频频谱。在这个框架内,我们的目标是通过优化长期无线电资源分配,最大限度地提高信息技术数据率,保证一定的FL聚合功能。一个关键挑战在于,由于服务器和边端设备之间频繁的沟通而导致的大型间接费用。要应对这一挑战,我们严格分析FL的计算-通信和通信系统在FL系统上对FL的计算和最直径路路路路路路路路路路路路路路比对FL的最优化分析。</s>