Federated edge learning is envisioned as the bedrock of enabling intelligence
in next-generation wireless networks, but the limited spectral resources often
constrain its scalability. In light of this challenge, a line of recent
research suggested integrating analog over-the-air computations into federated
edge learning systems, to exploit the superposition property of electromagnetic
waves for fast aggregation of intermediate parameters and achieve (almost)
unlimited scalability. Over-the-air computations also benefit the system in
other aspects, such as low hardware cost, reduced access latency, and enhanced
privacy protection. Despite these advantages, the interference introduced by
wireless communications also influences various aspects of the model training
process, while its importance is not well recognized yet. This article provides
a comprehensive overview of the positive and negative effects of interference
on over-the-air computation-based edge learning systems. The potential open
issues and research trends are also discussed.

该论文综述了使用模拟空中计算技术进行联邦边缘学习系统的正负影响和潜在问题。空中计算技术在快速聚合中间参数和实现（几乎）无限可扩展性方面具有优势，但无线通信引入的干扰也会影响模型训练过程的各个方面，同时其重要性尚未得到很好的认识。