Over-the-air computation (AirComp) based federated learning (FL) is capable
of achieving fast model aggregation by exploiting the waveform superposition
property of multiple access channels. However, the model aggregation
performance is severely limited by the unfavorable wireless propagation
channels. In this paper, we propose to leverage intelligent reflecting surface
(IRS) to achieve fast yet reliable model aggregation for AirComp-based FL. To
optimize the learning performance, we formulate an optimization problem that
jointly optimizes the device selection, the aggregation beamformer at the base
station (BS), and the phase shifts at the IRS to maximize the number of devices
participating in the model aggregation of each communication round under
certain mean-squared-error (MSE) requirements. To tackle the formulated
highly-intractable problem, we propose a two-step optimization framework.
Specifically, we induce the sparsity of device selection in the first step,
followed by solving a series of MSE minimization problems to find the maximum
feasible device set in the second step. We then propose an alternating
optimization framework, supported by the difference-of-convex-functions
programming algorithm for low-rank optimization, to efficiently design the
aggregation beamformers at the BS and phase shifts at the IRS. Simulation
results will demonstrate that our proposed algorithm and the deployment of an
IRS can achieve a lower training loss and higher FL prediction accuracy than
the baseline algorithms.

本文提出了一种基于智能反射表面的过空计算（AirComp）联邦学习（FL），通过联合优化设备选择、基站聚合波束成形器和 IRS 相位变化，以最大限度地提高每一轮通讯中参与模型聚合的设备数量，以达到在一定的均方误差（MSE）要求下快速、可靠的模型聚合。仿真结果表明，与基线算法相比，所提出的算法和 IRS 的部署可以实现更低的训练误差和更高的 FL 预测准确度。