# DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks

arXiv: https://arxiv.org/abs/2406.13533 · HTML: https://mlsanity.com/paper/2406.13533/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 67 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 30 of 40)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication](https://mlsanity.com/paper/1902.00340/) (2019)
- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [Stochastic Gradient Push for Distributed Deep Learning](https://mlsanity.com/paper/1811.10792/) (2018)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [Distributed optimization over time-varying directed graphs](https://mlsanity.com/paper/1303.2289/) (2013)
- [Decoupled Greedy Learning of CNNs](https://mlsanity.com/paper/1901.08164/) (2019)
- [DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training](https://mlsanity.com/paper/2206.00187/) (2022)
- [FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning](https://mlsanity.com/paper/2207.14130/) (2022)
- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [A Unified Analysis of Federated Learning with Arbitrary Client Participation](https://mlsanity.com/paper/2205.13648/) (2022)
- [Asynchronous Decentralized SGD with Quantized and Local Updates](https://mlsanity.com/paper/1910.12308/) (2019)
- [Gossip training for deep learning](https://mlsanity.com/paper/1611.09726/) (2016)
- [Cross-Gradient Aggregation for Decentralized Learning from Non-IID data](https://mlsanity.com/paper/2103.02051/) (2021)
- [Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphs](https://mlsanity.com/paper/1406.2075/) (2014)
- [Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization](https://mlsanity.com/paper/1804.06568/) (2018)
- [On the Convergence of Federated Averaging with Cyclic Client Participation](https://mlsanity.com/paper/2302.03109/) (2023)
- [Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks](https://mlsanity.com/paper/2012.05137/) (2020)
- [Communication-Efficient Topologies for Decentralized Learning with O(1) Consensus Rate](https://mlsanity.com/paper/2210.07881/) (2022)
- [Quantized Decentralized Stochastic Learning over Directed Graphs](https://mlsanity.com/paper/2002.09964/) (2020)
- [Personalized Decentralized Federated Learning with Knowledge Distillation](https://mlsanity.com/paper/2302.12156/) (2023)
- [Asynchronous Gradient-Push](https://mlsanity.com/paper/1803.08950/) (2018)
- [Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions](https://mlsanity.com/paper/1811.03982/) (2018)
- [A principled framework for the design and analysis of token algorithms](https://mlsanity.com/paper/2205.15015/) (2022)
- [Asynchronous Decentralized Learning over Unreliable Wireless Networks](https://mlsanity.com/paper/2202.00955/) (2022)
- [DIGEST: Fast and Communication Efficient Decentralized Learning with Local Updates](https://mlsanity.com/paper/2307.07652/) (2023)
- [Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization](https://mlsanity.com/paper/2311.00465/) (2023)
- [Decentralized Directed Collaboration for Personalized Federated Learning](https://mlsanity.com/paper/2405.17876/) (2024)
- [FROST -- Fast row-stochastic optimization with uncoordinated step-sizes](https://mlsanity.com/paper/1803.09169/) (2018)

## Built on by (most cited first, 5 of 5)

- [Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning](https://mlsanity.com/paper/2504.12181/) (2025)
- [Model-Consistent Byzantine-Resilient Decentralized Federated Learning for Collaborative Missions](https://mlsanity.com/paper/2608.22552/) (2026)
- [On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach](https://mlsanity.com/paper/2605.26162/) (2026)
- [Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints](https://mlsanity.com/paper/2606.28342/) (2026)
- [Agentic IoT: Architectures, Applications, and Challenges Toward the Internet of Agents](https://mlsanity.com/paper/2607.04219/) (2026)

## Abstract (arXiv)

Recent developments and emerging use cases, such as smart Internet of Things (IoT) and Edge AI, have sparked considerable interest in the training of neural networks over fully decentralized (serverless) networks. One of the major challenges of decentralized learning is to ensure stable convergence without resorting to strong assumptions applied for each agent regarding data distributions or updating policies. To address these issues, we propose DRACO, a novel method for decentralized asynchronous Stochastic Gradient Descent (SGD) over row-stochastic gossip wireless networks by leveraging continuous communication. Our approach enables edge devices within decentralized networks to perform local training and model exchanging along a continuous timeline, thereby eliminating the necessity for synchronized timing. The algorithm also features a specific technique of decoupling communication and computation schedules, which empowers complete autonomy for all users and manageable instructions for stragglers. Through a comprehensive convergence analysis, we highlight the advantages of asynchronous and autonomous participation in decentralized optimization. Our numerical experiments corroborate the efficacy of the proposed technique.