mlsanityData to 7 Oct 2026

Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning

arXiv 2306.10299 ↗First posted 17 Jun 2023cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 22 references that are in the map. First posted on arXiv 17 Jun 2023.

Builds on

13 of 22 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Learning via Over-the-Air Computationcited by 215
  3. 2019A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networkscited by 213
  4. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  5. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  6. 2018Wireless Network Intelligence at the Edgecited by 90
  7. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  8. 2022Anticorrelated Noise Injection for Improved Generalizationcited by 41
  9. 2020Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learningcited by 24
  10. 2021Over-the-Air Decentralized Federated Learningcited by 18
  11. 2021Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysiscited by 13
  12. 2021Wireless Federated Langevin Monte Carlo: Repurposing Channel Noise for Bayesian Sampling and Privacycited by 9
1 more
  1. 2021Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validationcited by 9

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Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024Adaptive Federated Learning Over the Aircited by 4
  2. 2024Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clippingcited by 3
  3. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
  4. 2025Rethinking Federated Learning Over the Air: The Blessing of Scaling Upcited by 1
  5. 2025Timely Parameter Updating in Over-the-Air Federated Learningcited by 0