Edge federated learning (FL) is an emerging paradigm that trains a global
parametric model from distributed datasets based on wireless communications.
This paper proposes a unit-modulus over-the-air computation (UMAirComp)
framework to facilitate efficient edge federated learning, which simultaneously
uploads local model parameters and updates global model parameters via analog
beamforming. The proposed framework avoids sophisticated baseband signal
processing, leading to low communication delays and implementation costs.
Training loss bounds of UMAirComp FL systems are derived and two low-complexity
large-scale optimization algorithms, termed penalty alternating minimization
(PAM) and accelerated gradient projection (AGP), are proposed to minimize the
nonconvex nonsmooth loss bound. Simulation results show that the proposed
UMAirComp framework with PAM algorithm achieves a smaller mean square error of
model parameters' estimation, training loss, and test error compared with other
benchmark schemes. Moreover, the proposed UMAirComp framework with AGP
algorithm achieves satisfactory performance while reduces the computational
complexity by orders of magnitude compared with existing optimization
algorithms. Finally, we demonstrate the implementation of UMAirComp in a
vehicle-to-everything autonomous driving simulation platform. It is found that
autonomous driving tasks are more sensitive to model parameter errors than
other tasks since the neural networks for autonomous driving contain sparser
model parameters.

本文提出了一种基于 UMAirComp 框架的的边缘联邦学习方式，该框架可利用模拟波束成形同时上传本地模型参数并更新全局模型参数，从而避免了复杂的基带信号处理，大大降低了通信延迟和实现成本。此外，还提出了一种面向非凸非光滑损失约束下的低复杂度大规模优化算法 PAM 和 AGP，并在基于 UMAirComp 框架的边缘联邦学习系统中获得较小的均方误差、训练损失和测试误差。最后，本文通过在车联网驾驶模拟平台中的测试表明，UMAirComp 系统对于自主驾驶任务比其他任务更加敏感由于自主行驶的神经网络包含更加稀疏的模型参数。