# Decentralized Federated Learning via Non-Coherent Over-the-Air Consensus

arXiv: https://arxiv.org/abs/2210.15806 · HTML: https://mlsanity.com/paper/2210.15806/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2022.

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

- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Non-Coherent Over-the-Air Decentralized Gradient Descent](https://mlsanity.com/paper/2211.10777/) (2022)
- [Biased Over-the-Air Federated Learning under Wireless Heterogeneity](https://mlsanity.com/paper/2403.19849/) (2024)
- [Interference-Robust Non-Coherent Over-the-Air Computation for Decentralized Optimization](https://mlsanity.com/paper/2602.12426/) (2026)

## Abstract (arXiv)

This paper presents NCOTA-DGD, a Decentralized Gradient Descent (DGD) algorithm that combines local gradient descent with a novel Non-Coherent Over-The-Air (NCOTA) consensus scheme to solve distributed machine-learning problems over wirelessly-connected systems. NCOTA-DGD leverages the waveform superposition properties of the wireless channels: it enables simultaneous transmissions under half-duplex constraints, by mapping local optimization signals to a mixture of preamble sequences, and consensus via non-coherent combining at the receivers. NCOTA-DGD operates without channel state information at transmitters and receivers, and leverages the average channel pathloss to mix signals, without explicit knowledge of the mixing weights (typically known in consensus-based optimization algorithms). It is shown both theoretically and numerically that, for smooth and strongly-convex problems with fixed consensus and learning stepsizes, the updates of NCOTA-DGD converge in Euclidean distance to the global optimum with rate mathcal O(K-1/4) for a target of K iterations. NCOTA-DGD is evaluated numerically over a logistic regression problem, showing faster convergence vis-`a-vis running time than implementations of the classical DGD algorithm over digital and analog orthogonal channels.