Federated edge learning (FEEL) has emerged as a core paradigm for large-scale optimization. However, FEEL still suffers from a communication bottleneck due to the transmission of high-dimensional model updates from the clients to the federator. Over-the-air computation (AirComp) leverages the additive property of multiple-access channels by aggregating the clients' updates over the channel to save communication resources. While analog uncoded transmission can benefit from the increased signal-to-noise ratio (SNR) due to the simultaneous transmission of many clients, potential errors may severely harm the learning process for small SNRs. To alleviate this problem, channel coding approaches were recently proposed for AirComp in FEEL. However, their error-correction capability degrades with an increasing number of clients. We propose a digital lattice-based code construction with constant error-correction capabilities in the number of clients, and compare to nested-lattice codes, well-known for their optimal rate and power efficiency in the point-to-point AWGN channel.

翻译：联邦边缘学习已成为大规模优化的核心范式。然而，由于客户端需向联邦服务器传输高维模型更新，联邦边缘学习仍面临通信瓶颈问题。空中计算利用多址信道的叠加特性，通过在信道上聚合客户端更新以节省通信资源。虽然模拟无编码传输可因众多客户端同时传输而受益于信噪比的提升，但在低信噪比条件下，潜在误差可能严重损害学习过程。为缓解此问题，近期已有研究提出将信道编码方法用于联邦边缘学习中的空中计算。然而，其纠错能力会随客户端数量增加而下降。我们提出一种基于数字格码的构造方案，其纠错能力在客户端数量变化时保持恒定，并与嵌套格码进行比较——后者因其在点对点加性高斯白噪声信道中的最优速率和功率效率而广为人知。