# Distributed Learning over a Wireless Network with FSK-Based Majority Vote

arXiv: https://arxiv.org/abs/2111.01850 · HTML: https://mlsanity.com/paper/2111.01850/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2021.

## Built on by (most cited first, 13 of 13)

- [A Survey on Over-the-Air Computation](https://mlsanity.com/paper/2210.11350/) (2022)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning](https://mlsanity.com/paper/2210.07012/) (2022)
- [Blind Federated Learning via Over-the-Air q-QAM](https://mlsanity.com/paper/2311.04253/) (2023)
- [A Demonstration of Over-the-Air Computation for Federated Edge Learning](https://mlsanity.com/paper/2209.09954/) (2022)
- [Over-the-Air Computation over Balanced Numerals](https://mlsanity.com/paper/2209.11004/) (2022)
- [VecComp: Vector Computing via MIMO Digital Over-the-Air Computation](https://mlsanity.com/paper/2511.02765/) (2025)
- [Over-the-Air Computation in OFDM Systems with Imperfect Channel State Information](https://mlsanity.com/paper/2307.05357/) (2023)
- [Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation](https://mlsanity.com/paper/2308.02881/) (2023)
- [Learned Digital Over-the-Air Computing for Federated Edge Learning](https://mlsanity.com/paper/2509.16577/) (2025)
- [Learned Digital Codes for Over-the-Air Computation in Federated Edge Learning](https://mlsanity.com/paper/2512.19777/) (2025)
- [OptiVote: Non-Coherent FSO Over-the-Air Majority Vote for Communication-Efficient Distributed Federated Learning in Space Data Centers](https://mlsanity.com/paper/2512.24334/) (2025)

## Abstract (arXiv)

In this study, we propose an over-the-air computation (AirComp) scheme for federated edge learning (FEEL). The proposed scheme relies on the concept of distributed learning by majority vote (MV) with sign stochastic gradient descend (signSGD). As compared to the state-of-the-art solutions, with the proposed method, edge devices (EDs) transmit the signs of local stochastic gradients by activating one of two orthogonal resources, i.e., orthogonal frequency division multiplexing (OFDM) subcarriers, and the MVs at the edge server (ES) are obtained with non-coherent detectors by exploiting the energy accumulations on the subcarriers. Hence, the proposed scheme eliminates the need for channel state information (CSI) at the EDs and ES. By taking path loss, power control, cell size, and the probabilistic nature of the detected MVs in fading channel into account, we prove the convergence of the distributed learning for a non-convex function. Through simulations, we show that the proposed scheme can provide a high test accuracy in fading channels even when the time-synchronization and the power alignment at the ES are not ideal. We also provide insight into distributed learning for location-dependent data distribution for the MV-based schemes.