We study over-the-air model aggregation in federated edge learning (FEEL) systems, where channel state information at the transmitters (CSIT) is assumed to be unavailable. We leverage the reconfigurable intelligent surface (RIS) technology to align the cascaded channel coefficients for CSIT-free model aggregation. To this end, we jointly optimize the RIS and the receiver by minimizing the aggregation error under the channel alignment constraint. We then develop a difference-of-convex algorithm for the resulting non-convex optimization. Numerical experiments on image classification show that the proposed method is able to achieve a similar learning accuracy as the state-of-the-art CSIT-based solution, demonstrating the efficiency of our approach in combating the lack of CSIT.

中文翻译：

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通过可重新配置的智能表面实现无CSIT的联合边缘学习

我们研究了联邦边缘学习（FEEL）系统中的空中模型聚合，其中假定发射机（CSIT）的信道状态信息不可用。我们利用可重构智能表面（RIS）技术来对齐级联的信道系数，以实现无CSIT的模型聚合。为此，我们通过最小化信道对准约束下的聚合误差来共同优化RIS和接收器。然后，我们针对最终的非凸优化开发凸差分算法。图像分类的数值实验表明，所提出的方法能够获得与基于CSIT的最新解决方案类似的学习精度，这证明了我们的方法在解决CSIT缺乏方面的效率。