Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach
Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach
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DOI：
10.1109/twc.2021.3086116
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发表时间：
2020-11
影响因子：
10.4
通讯作者：
Hang Liu;Xiaojun Yuan;Y. Zhang
中科院分区：
文献类型：
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作者：
Hang Liu;Xiaojun Yuan;Y. Zhang
为了利用移动边缘网络产生的海量数据，联邦学习(FL)被提出作为集中式机器学习(ML)的有吸引力的替代品。通过在边缘设备上协作训练共享学习模型，FL避免了直接的数据传输，从而克服了与集中式ML相比的高通信延迟和隐私问题。为了提高FL模型聚合中的通信效率，利用无线信道固有的叠加特性，引入了空中计算来支持大量本地模型的同时上传。然而，由于边缘设备之间通信容量的异构性，空中FL存在信道最弱的设备成为模型聚合性能瓶颈的散布问题。这个问题可以通过设备选择在一定程度上得到缓解，但后者仍然需要在数据利用和模型通信之间进行权衡。在本文中，我们利用可重构智能表面(RIS)技术来缓解空中FL中的掉队问题。具体地说，我们开发了一个学习分析框架来定量表征设备选择和模型聚合误差对空中FL收敛的影响。然后，我们制定了一个统一的通信学习优化问题，对设备选择、空中收发设计和RIS配置进行联合优化。数值实验表明，与最先进的方法相比，所提出的设计获得了显著的学习精度改进，特别是当边缘设备的信道条件变化很大时。
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.