Edge Intelligence Over the Air: Two Faces of Interference in Federated
  Learning
Zihan Chen Howard H. Yang Tony Q. S. Quek
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.