# Decentralized Learning over Wireless Networks with Broadcast-Based Subgraph Sampling

arXiv: https://arxiv.org/abs/2310.16106 · HTML: https://mlsanity.com/paper/2310.16106/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Decentralized Deep Learning with Arbitrary Communication Compression](https://mlsanity.com/paper/1907.09356/) (2019)
- [Communication Compression for Decentralized Training](https://mlsanity.com/paper/1803.06443/) (2018)
- [Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD](https://mlsanity.com/paper/1810.08313/) (2018)
- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [Decentralized gradient methods: does topology matter?](https://mlsanity.com/paper/2002.12688/) (2020)
- [Convergence rates for distributed stochastic optimization over random networks](https://mlsanity.com/paper/1803.07836/) (2018)
- [Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima](https://mlsanity.com/paper/2003.02818/) (2020)
- [Distributed Consensus in Wireless Networks with Probabilistic Broadcast Scheduling](https://mlsanity.com/paper/2301.11714/) (2023)

## Built on by (most cited first, 2 of 2)

- [Faster Convergence with Less Communication: Broadcast-Based Subgraph Sampling for Decentralized Learning over Wireless Networks](https://mlsanity.com/paper/2401.13779/) (2024)
- [Information Entropy-Based Scheduling for Communication-Efficient Decentralized Learning](https://mlsanity.com/paper/2507.17426/) (2025)

## Abstract (arXiv)

This work centers on the communication aspects of decentralized learning over wireless networks, using consensus-based decentralized stochastic gradient descent (D-SGD). Considering the actual communication cost or delay caused by in-network information exchange in an iterative process, our goal is to achieve fast convergence of the algorithm measured by improvement per transmission slot. We propose BASS, an efficient communication framework for D-SGD over wireless networks with broadcast transmission and probabilistic subgraph sampling. In each iteration, we activate multiple subsets of non-interfering nodes to broadcast model updates to their neighbors. These subsets are randomly activated over time, with probabilities reflecting their importance in network connectivity and subject to a communication cost constraint (e.g., the average number of transmission slots per iteration). During the consensus update step, only bi-directional links are effectively preserved to maintain communication symmetry. In comparison to existing link-based scheduling methods, the inherent broadcasting nature of wireless channels offers intrinsic advantages in speeding up convergence of decentralized learning by creating more communicated links with the same number of transmission slots.