# Decentralized Learning over Wireless Networks: The Effect of Broadcast with Random Access

arXiv: https://arxiv.org/abs/2305.07368 · HTML: https://mlsanity.com/paper/2305.07368/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 5 of 5)

- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [On the Convergence of Decentralized Gradient Descent](https://mlsanity.com/paper/1310.7063/) (2013)
- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [A general framework for decentralized optimization with first-order methods](https://mlsanity.com/paper/2009.05837/) (2020)
- [Asynchronous Decentralized Learning over Unreliable Wireless Networks](https://mlsanity.com/paper/2202.00955/) (2022)

## Built on by (most cited first, 3 of 3)

- [Faster Convergence with Less Communication: Broadcast-Based Subgraph Sampling for Decentralized Learning over Wireless Networks](https://mlsanity.com/paper/2401.13779/) (2024)
- [Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning](https://mlsanity.com/paper/2512.24069/) (2025)
- [Optimizing Stochastic Gradient Push under Broadcast Communications](https://mlsanity.com/paper/2604.15549/) (2026)

## Abstract (arXiv)

In this work, we focus on the communication aspect of decentralized learning, which involves multiple agents training a shared machine learning model using decentralized stochastic gradient descent (D-SGD) over distributed data. In particular, we investigate the impact of broadcast transmission and probabilistic random access policy on the convergence performance of D-SGD, considering the broadcast nature of wireless channels and the link dynamics in the communication topology. Our results demonstrate that optimizing the access probability to maximize the expected number of successful links is a highly effective strategy for accelerating the system convergence.