We study federated learning (FL) over wireless fading channels where multiple devices simultaneously send their model updates. We propose an efficient \emph{age-aware edge-blind over-the-air FL} approach that does not require channel state information (CSI) at the devices. Instead, the parameter server (PS) uses multiple antennas and applies maximum-ratio combining (MRC) based on its estimated sum of the channel gains to detect the parameter updates. A key challenge is that the number of orthogonal subcarriers is limited; thus, transmitting many parameters requires multiple Orthogonal Frequency Division Multiplexing (OFDM) symbols, which increases latency. To address this, the PS selects only a small subset of model coordinates each round using \emph{AgeTop-\(k\)}, which first picks the largest-magnitude entries and then chooses the \(k\) coordinates with the longest waiting times since they were last selected. This ensures that all selected parameters fit into a single OFDM symbol, reducing latency. We provide a convergence bound that highlights the advantages of using a higher number of antenna array elements and demonstrates a key trade-off: increasing \(k\) decreases compression error at the cost of increasing the effect of channel noise. Experimental results show that (i) more PS antennas greatly improve accuracy and convergence speed; (ii) AgeTop-\(k\) outperforms random selection under relatively good channel conditions; and (iii) the optimum \(k\) depends on the channel, with smaller \(k\) being better in noisy settings.

翻译：我们研究了在无线衰落信道下的联邦学习（FL），其中多个设备同时发送其模型更新。我们提出了一种高效的**基于年龄感知的边缘盲空中联邦学习**方法，该方法不需要设备端获取信道状态信息（CSI）。相反，参数服务器（PS）使用多天线，并基于其估计的信道增益总和应用最大比合并（MRC）来检测参数更新。一个关键挑战在于正交子载波的数量有限；因此，传输大量参数需要多个正交频分复用（OFDM）符号，这会增加延迟。为解决这一问题，PS在每一轮中仅使用**AgeTop-\(k\)**选择一小部分模型坐标，该算法首先选取幅度最大的条目，然后选择自上次被选中以来等待时间最长的\(k\)个坐标。这确保了所有选定的参数都能容纳在单个OFDM符号中，从而降低了延迟。我们提供了一个收敛界，突出了使用更多天线阵列元素的优势，并展示了一个关键权衡：增加\(k\)会减少压缩误差，但代价是增加了信道噪声的影响。实验结果表明：（i）更多的PS天线能显著提高准确性和收敛速度；（ii）在相对较好的信道条件下，AgeTop-\(k\)优于随机选择；（iii）最优的\(k\)取决于信道条件，在噪声较大的环境中，较小的\(k\)表现更佳。