To exploit massive amounts of data generated at mobile edge networks, federated learning (FL) has been proposed as an attractive substitute for centralized machine learning (ML). By collaboratively training a shared learning model at edge devices, FL avoids direct data transmission and thus overcomes high communication latency and privacy issues as compared to centralized ML. To improve the communication efficiency in FL model aggregation, over-the-air computation has been introduced to support a large number of simultaneous local model uploading by exploiting the inherent superposition property of wireless channels. However, due to the heterogeneity of communication capacities among edge devices, over-the-air FL suffers from the straggler issue in which the device with the weakest channel acts as a bottleneck of the model aggregation performance. This issue can be alleviated by device selection to some extent, but the latter still suffers from a tradeoff between data exploitation and model communication. In this paper, we leverage the reconfigurable intelligent surface (RIS) technology to relieve the straggler issue in over-the-air FL. Specifically, we develop a learning analysis framework to quantitatively characterize the impact of device selection and model aggregation error on the convergence of over-the-air FL. Then, we formulate a unified communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration. Numerical experiments show that the proposed design achieves substantial learning accuracy improvements compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.
翻译:为了利用移动边缘网络产生的大量数据,提议采用联合学习(FL)作为集中机器学习的有吸引力的替代工具。通过在边缘设备上合作培训共享学习模式,FL避免直接数据传输,从而克服与中央ML相比的高度通信延迟和隐私问题。为了提高FL模型集成的通信效率,引入了超空计算,以支持大量同时同时上传的本地模型,利用无线频道固有的超定位属性。然而,由于边缘设备通信能力的异质性能,高空FL受到在边缘设备上共享学习模式问题的影响,FL避免直接数据传输,从而克服了与中央集成性功能相比的共享学习模式。我们开发了一个学习框架,与最弱频道的系统相比,在设计中最差的系统性能上,我们通过模拟和最优化的系统选择模型和模型的深度分析,我们随后的系统选择模型和最优化的系统选择,从而形成一个相对最优化的模型和最优化的系统选择。