arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2011.05051cs.ITcs.LGeess.SPmath.IT
收藏

基于智能反射面的联邦学习

Federated Learning via Intelligent Reflecting Surface

ShanghaiTech University(上海科技大学);Xiamen University(厦门大学);Tsinghua University(清华大学);Hong Kong University of Science and Technology(香港科技大学);Peng Cheng Laboratory(鹏城实验室)

Zhibin Wang, Jiahang Qiu, Yong Zhou, Yuanming Shi, Liqun Fu, Wei Chen, Khaled B. Lataief

摘要:

基于空中计算(AirComp)的联邦学习(FL)能够利用多个接入信道的波形叠加特性实现快速模型聚合。然而,模型聚合性能受到不利的无线传播信道的严重限制。在本文中,我们提出利用智能反射表面(IRS)来实现基于AirComp的FL的快速而可靠的模型聚合。为了优化学习性能,我们制定了一个优化问题,该优化问题联合优化设备选择,基站(BS)处的聚合波束形成器,以及IRS处的相移,以在特定均方误差(MSE)要求下最大化参与每个通信回合的模型聚合的设备的数量。为了解决制定的高度棘手的问题,我们提出了一个两步优化框架。具体来说,我们在第一步中诱导设备选择的稀疏性,然后在第二步中解决一系列MSE最小化问题以找到最大可行设备集。然后,我们提出了一个交替的优化框架,支持的低秩优化的凸函数的差分编程算法,有效地设计聚合波束形成器在BS和相位移在IRS。仿真结果将表明,与基线算法相比,我们提出的算法和IRS的部署可以实现更低的训练损失和更高的FL预测准确度。

英文摘要:

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.

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。

↑