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

arXiv: https://arxiv.org/abs/2209.09954 · HTML: https://mlsanity.com/paper/2209.09954/

## 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 (most cited first, 7 of 7)

- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis](https://mlsanity.com/paper/2001.05713/) (2020)
- [5G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technology](https://mlsanity.com/paper/1806.06898/) (2018)
- [Distributed Learning over a Wireless Network with FSK-Based Majority Vote](https://mlsanity.com/paper/2111.01850/) (2021)
- [Distributed Learning over a Wireless Network with Non-coherent Majority Vote Computation](https://mlsanity.com/paper/2209.04692/) (2022)
- [Multi-cell Non-coherent Over-the-Air Computation for Federated Edge Learning](https://mlsanity.com/paper/2202.00506/) (2022)
- [AP-initiated Multi-User Transmissions in IEEE 802.11ax WLANs](https://mlsanity.com/paper/1702.05397/) (2017)

## Built on by (most cited first, 4 of 4)

- [Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning](https://mlsanity.com/paper/2210.07012/) (2022)
- [Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks](https://mlsanity.com/paper/2503.19549/) (2025)
- [Timely Parameter Updating in Over-the-Air Federated Learning](https://mlsanity.com/paper/2512.19103/) (2025)
- [SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillation](https://mlsanity.com/paper/2602.15326/) (2026)

## 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.