We study federated learning (FL) over wireless fading channels where multiple devices simultaneously send their model updates. We propose an efficient 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 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：我们研究了无线衰落信道上的联邦学习（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年龄感知边缘盲空中联邦学习方法，该方法无需设备端获取信道状态信息（CSI）。参数服务器（PS）利用多天线技术，基于其估计的信道增益和进行最大比合并（MRC）以检测参数更新。关键挑战在于正交子载波数量有限，因此传输大量参数需占用多个正交频分复用（OFDM）符号，导致时延增加。为解决此问题，PS每轮仅使用AgeTop-k选择模型坐标的少量子集：该算法首先选取幅度最大的条目，然后根据各坐标距离上次被选中的等待时长选择k个坐标。这确保所有选定参数可在一个OFDM符号内完成传输，降低时延。我们提供了收敛界，表明增加天线阵列单元数量的优势，并揭示关键权衡：增大k会降低压缩误差，但会加剧信道噪声的影响。实验结果表明：（i）增加PS天线数量可显著提升精度与收敛速度；（ii）在信道条件较好时，AgeTop-k优于随机选择策略；（iii）最优k值取决于信道状态，噪声环境下较小的k值表现更优。