mlsanityData to 7 Oct 2026

Decentralized Federated Learning via SGD over Wireless D2D Networks

arXiv 2002.12507 ↗First posted 28 Feb 2020cs.IT cs.NI eess.SPcited by 32

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Abstract (arXiv)

Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network edge, enables joint training of a machine learning model over distributed data sets and computing resources with limited disclosure of local data. Communication is a critical enabler of large-scale FL due to significant amount of model information exchanged among edge devices. In this paper, we consider a network of wireless devices sharing a common fading wireless channel for the deployment of FL. Each device holds a generally distinct training set, and communication typically takes place in a Device-to-Device (D2D) manner. In the ideal case in which all devices within communication range can communicate simultaneously and noiselessly, a standard protocol that is guaranteed to converge to an optimal solution of the global empirical risk minimization problem under convexity and connectivity assumptions is Decentralized Stochastic Gradient Descent (DSGD). DSGD integrates local SGD steps with periodic consensus averages that require communication between neighboring devices. In this paper, wireless protocols are proposed that implement DSGD by accounting for the presence of path loss, fading, blockages, and mutual interference. The proposed protocols are based on graph coloring for scheduling and on both digital and analog transmission strategies at the physical layer, with the latter leveraging over-the-air computing via sparsity-based recovery.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2020.

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Citing papers per year: 2021: 8; 2022: 10; 2023: 7; 2024: 1; 2025: 3; 2026: 3.

32 papers.

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  1. 2021Decentralized Federated Averagingcited by 114
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
  4. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
  5. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  6. 2022On the (In)security of Peer-to-Peer Decentralized Machine Learningcited by 23
  7. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  8. 2021Over-the-Air Decentralized Federated Learningcited by 18
  9. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  10. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  11. 2022Asynchronous Decentralized Learning over Unreliable Wireless Networkscited by 13
  12. 2022Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computingcited by 13
20 more
  1. 2023Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Predictioncited by 9
  2. 2021A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learningcited by 8
  3. 2022Confederated Learning: Federated Learning with Decentralized Edge Serverscited by 7
  4. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  5. 2022Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relayingcited by 6
  6. 2021The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communicationcited by 5
  7. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  8. 2025Cooperative Decentralized Backdoor Attacks on Vertical Federated Learningcited by 3
  9. 2023Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clippingcited by 2
  10. 2022UAV-Aided Decentralized Learning over Mesh Networkscited by 1
  11. 2022To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computingcited by 1
  12. 2023Data Games: A Game-Theoretic Approach to Swarm Robotic Data Collectioncited by 1
  13. 2023Collaborative Distributed Machine Learningcited by 1
  14. 2024Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocolscited by 1
  15. 2025FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classificationcited by 1
  16. 2021Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Samplingcited by 0
  17. 2023The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistenciescited by 0
  18. 2026Joint Routing and Model Pruning for Decentralized Federated Learning in Bandwidth-Constrained Multi-Hop Wireless Networkscited by 0
  19. 2026LoDAdaC: a unified local training-based decentralized framework with adaptive gradients and compressed communicationcited by 0
  20. 2026When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated Learningcited by 0