mlsanityData to 14 Oct 2026

A Demonstration of Over-the-Air Computation for Federated Edge Learning

arXiv 2209.09954 ↗First posted 20 Sep 2022eess.SP cs.AI cs.ITcited by 4

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Abstract (arXiv)

In this study, we propose a general-purpose synchronization method that allows a set of software-defined radios (SDRs) to transmit or receive any in-phase/quadrature data with precise timings while maintaining the baseband processing in the corresponding companion computers. The proposed method relies on the detection of a synchronization waveform in both receive and transmit directions and controlling the direct memory access blocks jointly with the processing system. By implementing this synchronization method on a set of low-cost SDRs, we demonstrate the performance of frequency-shift keying (FSK)-based majority vote (MV), i.e., an over-the-air computation scheme for federated edge learning, and introduce the corresponding procedures. Our experiment shows that the test accuracy can reach more than 95% for homogeneous and heterogeneous data distributions without using channel state information at the edge devices.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 20 Sep 2022.

Builds on

7 of 17 references.

References · 7

  1. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  2. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  3. 20185G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technologycited by 16
  4. 2021Distributed Learning over a Wireless Network with FSK-Based Majority Votecited by 13
  5. 2022Distributed Learning over a Wireless Network with Non-coherent Majority Vote Computationcited by 9
  6. 2022Multi-cell Non-coherent Over-the-Air Computation for Federated Edge Learningcited by 4
  7. 2017AP-initiated Multi-User Transmissions in IEEE 802.11ax WLANscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2022Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learningcited by 10
  2. 2025Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networkscited by 1
  3. 2025Timely Parameter Updating in Over-the-Air Federated Learningcited by 0
  4. 2026SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillationcited by 0