mlsanityData to 7 Oct 2026

Over-the-Air Decentralized Federated Learning

arXiv 2106.08011 ↗First posted 15 Jun 2021cs.IT cs.LG eess.SPcited by 18

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Abstract (arXiv)

In this paper, we consider decentralized federated learning (FL) over wireless networks, where over-the-air computation (AirComp) is adopted to facilitate the local model consensus in a device-to-device (D2D) communication manner. However, the AirComp-based consensus phase brings the additive noise in each algorithm iterate and the consensus needs to be robust to wireless network topology changes, which introduce a coupled and novel challenge of establishing the convergence for wireless decentralized FL algorithm. To facilitate consensus phase, we propose an AirComp-based DSGD with gradient tracking and variance reduction (DSGT-VR) algorithm, where both precoding and decoding strategies are developed for D2D communication. Furthermore, we prove that the proposed algorithm converges linearly and establish the optimality gap for strongly convex and smooth loss functions, taking into account the channel fading and noise. The theoretical result shows that the additional error bound in the optimality gap depends on the number of devices. Extensive simulations verify the theoretical results and show that the proposed algorithm outperforms other benchmark decentralized FL algorithms over wireless networks.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 25 of its 39 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

25 of 39 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 496
  4. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  5. 2020A Unified Theory of Decentralized SGD with Changing Topology and Local Updatescited by 361
  6. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  7. 2018Federated Learning via Over-the-Air Computationcited by 215
  8. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  9. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  10. 2018Distributed Stochastic Gradient Tracking Methodscited by 106
  11. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
  12. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
13 more
  1. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
  2. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  3. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  4. 2019Variance-Reduced Decentralized Stochastic Optimization with Accelerated Convergencecited by 33
  5. 2020Decentralized Federated Learning via SGD over Wireless D2D Networkscited by 32
  6. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  7. 2020Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surfacecited by 21
  8. 2020Decentralized SGD with Over-the-Air Computationcited by 16
  9. 2020Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learningcited by 15
  10. 2021Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theorycited by 9
  11. 2018Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flowcited by 8
  12. 2020D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edgecited by 5
  13. 2020Over-the-Air Computing for Wireless Data Aggregation in Massive IoTcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 6; 2024: 1; 2025: 4; 2026: 1.

18 papers.

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  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  4. 2023Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognitioncited by 19
  5. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  6. 2022Asynchronous Decentralized Learning over Unreliable Wireless Networkscited by 13
  7. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  8. 2025Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Frameworkcited by 10
  9. 2022Non-Coherent Over-the-Air Decentralized Gradient Descentcited by 9
  10. 2021Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channelscited by 7
  11. 2023Edge Intelligence Over the Air: Two Faces of Interference in Federated Learningcited by 5
  12. 2025Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learningcited by 2
6 more
  1. 2022UAV-Aided Decentralized Learning over Mesh Networkscited by 1
  2. 2022Over-The-Air Clustered Wireless Federated Learningcited by 1
  3. 2024Optimal Batch Allocation for Wireless Federated Learningcited by 1
  4. 2025FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classificationcited by 1
  5. 2025DFCA: Decentralized Federated Clustering Algorithmcited by 0
  6. 2026Interference-Robust Non-Coherent Over-the-Air Computation for Decentralized Optimizationcited by 0