# Distributed Learning over a Wireless Network with Non-coherent Majority Vote Computation

arXiv: https://arxiv.org/abs/2209.04692 · HTML: https://mlsanity.com/paper/2209.04692/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2022.

## Built on by (most cited first, 9 of 9)

- [A Survey on Over-the-Air Computation](https://mlsanity.com/paper/2210.11350/) (2022)
- [Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning](https://mlsanity.com/paper/2210.07012/) (2022)
- [A Demonstration of Over-the-Air Computation for Federated Edge Learning](https://mlsanity.com/paper/2209.09954/) (2022)
- [Wireless Federated k-Means Clustering with Non-coherent Over-the-Air Computation](https://mlsanity.com/paper/2308.06371/) (2023)
- [Timely Parameter Updating in Over-the-Air Federated Learning](https://mlsanity.com/paper/2512.19103/) (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)
- [SCENE OTA-FD: Self-Centering Noncoherent Estimator for Over-the-Air Federated Distillation](https://mlsanity.com/paper/2602.15326/) (2026)
- [MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computation](https://mlsanity.com/paper/2602.18332/) (2026)
- [Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning](https://mlsanity.com/paper/2605.07263/) (2026)

## Abstract (arXiv)

In this study, we propose an over-the-air computation (OAC) scheme to calculate the majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edge devices (EDs) transmit the signs of local stochastic gradients, i.e., votes, by activating one of two orthogonal resources. The MVs at the edge server (ES) are obtained with non-coherent detectors by exploiting the accumulations on the resources. Hence, the proposed scheme eliminates the need for channel state information (CSI) at the EDs and ES. In this study, we analyze various gradient-encoding strategies through the weight functions and waveform configurations over orthogonal frequency division multiplexing (OFDM). We show that specific weight functions that enable absentee EDs (i.e., hard-coded participation with absentees (HPA)) or weighted votes (i.e., soft-coded participation (SP)) can substantially reduce the probability of detecting the incorrect MV. By taking path loss, power control, cell size, and fading channel into account, we prove the convergence of the distributed learning for a non-convex function for HPA. Through simulations, we show that the proposed scheme with HPA and SP can provide high test accuracy even when the time-synchronization and the power control are not ideal under heterogeneous data distribution scenarios.