arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2011.10282cs.ITcs.LGcs.NIeess.SPmath.IT
收藏

可重构智能表面赋能的联邦学习:一种通信-学习一体化设计方法

Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach

The Chinese University of Hong Kong(香港中文大学);the University of Electronic Science and Technology of China(电子科技大学)

Hang Liu, Xiaojun Yuan, Ying-Jun Angela Zhang

AI总结:

针对空中联邦学习的掉队者问题,提出利用可重构智能表面技术,通过构建学习分析框架并联合优化设备选择、收发机设计与RIS配置,显著提升学习精度。

摘要:

为了挖掘移动边缘网络中产生的海量数据的价值,联邦学习(FL)已被提出作为集中式机器学习(ML)的一种有吸引力的替代方案。通过在边缘设备处协同训练共享学习模型,与集中式ML相比,FL避免了直接的数据传输,从而克服了高通信延迟和隐私问题。为了提升FL模型聚合的通信效率,空中计算已被引入,其利用无线信道的固有叠加特性,支持大量设备同时上传本地模型。然而,由于边缘设备之间通信能力的异构性,空中FL面临掉队者问题,即信道最弱的设备会成为模型聚合性能的瓶颈。该问题可通过设备选择在一定程度上得到缓解,但设备选择方法仍面临数据利用与模型通信之间的权衡问题。在本文中,我们利用可重构智能表面(RIS)技术来缓解空中FL中的掉队者问题。具体而言,我们构建了一个学习分析框架,以定量表征设备选择和模型聚合误差对空中FL收敛性的影响。随后,我们构建了一个通信-学习联合优化问题,以联合优化设备选择、空中收发机设计和RIS配置。数值实验表明,与现有最先进的方法相比,所提出的设计实现了显著的学习精度提升,尤其是在边缘设备间信道条件差异极大的情况下。

英文摘要:

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 improvement compared with the state-of-the-art approaches, especially when channel conditions vary dramatically across edge devices.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑