mlsanityData to 7 Oct 2026

Asynchronous Decentralized Learning over Unreliable Wireless Networks

arXiv 2202.00955 ↗First posted 2 Feb 2022cs.IT cs.LG eess.SPcited by 13

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Abstract (arXiv)

Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireless networks with fixed topologies and reliable workers. In this work, we propose an asynchronous decentralized stochastic gradient descent (DSGD) algorithm, which is robust to the inherent computation and communication failures occurring at the wireless network edge. We theoretically analyze its performance and establish a non-asymptotic convergence guarantee. Experimental results corroborate our analysis, demonstrating the benefits of asynchronicity and outdated gradient information reuse in decentralized learning over unreliable wireless networks.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 2 Feb 2022.

Builds on

9 of 14 references.

References · 9

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 361
  3. 2019Decentralized Deep Learning with Arbitrary Communication Compressioncited by 109
  4. 2018Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGDcited by 68
  5. 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
  6. 2020Decentralized Federated Learning via SGD over Wireless D2D Networkscited by 32
  7. 2021Over-the-Air Decentralized Federated Learningcited by 18
  8. 2020Decentralized SGD with Over-the-Air Computationcited by 16
  9. 2021Decentralized optimization with non-identical sampling in presence of stragglerscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 2; 2024: 3; 2025: 1; 2026: 3.

13 papers.

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  1. 2022Communication-Efficient Distributionally Robust Decentralized Learningcited by 10
  2. 2022Non-Coherent Over-the-Air Decentralized Gradient Descentcited by 9
  3. 2024Boosting Asynchronous Decentralized Learning with Model Fragmentationcited by 6
  4. 2024Decentralized Federated Learning Over Imperfect Communication Channelscited by 5
  5. 2024DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networkscited by 5
  6. 2023Decentralized Learning over Wireless Networks: The Effect of Broadcast with Random Accesscited by 3
  7. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  8. 2025Argus: Federated Non-convex Bilevel Learning over 6G Space-Air-Ground Integrated Networkcited by 2
  9. 2022UAV-Aided Decentralized Learning over Mesh Networkscited by 1
  10. 2022UAV-Aided Multi-Community Federated Learningcited by 1
  11. 2026Interference-Robust Non-Coherent Over-the-Air Computation for Decentralized Optimizationcited by 0
  12. 2026Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneitiescited by 0
1 more
  1. 2026Decentralized Gradient Descent: Bottleneck Regimes and Budget Complexitycited by 0