论文标题
通过高精油的空中计算，能量和频谱有效的联合学习
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation
论文作者
论文摘要
联合学习（FL）使移动设备能够在保留本地数据的同时协作学习共享的预测模型。但是，实际上在移动设备上部署FL存在两个主要的研究挑战：（i）频繁的无线梯度更新V.S.频谱资源有限，以及（ii）培训期间的渴望fl沟通和本地计算电池约束的移动设备。为了应对这些挑战，在本文中，我们提出了一种新型的多位空天空计算（M-AIRCOMP）方法，用于FL中局部模型更新的频谱有效聚合，并进一步介绍了用于移动设备的能源有效的FL设计。具体而言，高精度数字调制方案是在MAIRCOMP中设计和合并的，允许移动设备同时在多访问通道中同时将模型更新上传。此外，我们理论上分析了FL算法的收敛性。在FL收敛分析的指导下，我们制定了联合传输概率和局部计算控制优化，旨在最大程度地减少FL移动设备的总体能耗（即，迭代局部计算 +多轮通信）。广泛的仿真结果表明，我们所提出的方案在频谱利用率，能源效率和学习准确性方面优于现有计划。
Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.
