mlsanityData to 14 Oct 2026

Peer-to-peer Federated Learning on Graphs

arXiv 1901.11173 ↗First posted 31 Jan 2019cs.LG stat.MLcited by 85

Not on the map. Why?

Abstract (arXiv)

We consider the problem of training a machine learning model over a network of nodes in a fully decentralized framework. The nodes take a Bayesian-like approach via the introduction of a belief over the model parameter space. We propose a distributed learning algorithm in which nodes update their belief by aggregate information from their one-hop neighbors to learn a model that best fits the observations over the entire network. In addition, we also obtain sufficient conditions to ensure that the probability of error is small for every node in the network. We discuss approximations required for applying this algorithm to train Deep Neural Networks (DNNs). Experiments on training linear regression model and on training a DNN show that the proposed learning rule algorithm provides a significant improvement in the accuracy compared to the case where nodes learn without cooperation.

Key facts. Cited by 85 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Jan 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 18; 2022: 22; 2023: 17; 2024: 10; 2025: 11; 2026: 7.

85 papers.

Sort:

By area:

  1. 2021Decentralized Federated Averagingcited by 114
  2. 2021Federated Graph Classification over Non-IID Graphscited by 114
  3. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  4. 2021Federated Quantum Machine Learningcited by 82
  5. 2022DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Trainingcited by 74
  6. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  7. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  8. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  9. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45
  10. 2023Improving the Model Consistency of Decentralized Federated Learningcited by 45
  11. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  12. 2023Inductive Graph Unlearningcited by 31
38 more
  1. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  2. 2022Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matchingcited by 24
  3. 2022On the (In)security of Peer-to-Peer Decentralized Machine Learningcited by 23
  4. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  5. 2021Source Free Unsupervised Graph Domain Adaptationcited by 18
  6. 2021Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Spacecited by 17
  7. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
  8. 2022More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networkscited by 15
  9. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  10. 2022Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graphcited by 11
  11. 2023A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detectioncited by 10
  12. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  13. 2022Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholdscited by 9
  14. 2023Backdoor Attacks in Peer-to-Peer Federated Learningcited by 9
  15. 2021Separation of Powers in Federated Learningcited by 8
  16. 2022Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learningcited by 7
  17. 2022Federated Learning for Distributed Spectrum Sensing in NextG Communication Networkscited by 7
  18. 2022Confederated Learning: Federated Learning with Decentralized Edge Serverscited by 7
  19. 2023Flame: Simplifying Topology Extension in Federated Learningcited by 7
  20. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  21. 2022Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learningcited by 6
  22. 2022Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relayingcited by 6
  23. 2023DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learningcited by 6
  24. 2021FLoBC: A Decentralized Blockchain-Based Federated Learning Frameworkcited by 5
  25. 2022Network Gradient Descent Algorithm for Decentralized Federated Learningcited by 5
  26. 2023Asymmetrically Decentralized Federated Learningcited by 5
  27. 2022Asynchronous Hierarchical Federated Learningcited by 4
  28. 2022FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesiscited by 4
  29. 2023Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networkscited by 4
  30. 2025Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligencecited by 4
  31. 2025ADF-LoRA: Alternating Low-Rank Aggregation for Decentralized Federated Fine-Tuningcited by 4
  32. 2021Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systemscited by 3
  33. 2021Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffscited by 3
  34. 2022Bayesian data fusion with shared priorscited by 3
  35. 2023GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Datacited by 3
  36. 2023SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspectioncited by 3
  37. 2021DeceFL: A Principled Decentralized Federated Learning Frameworkcited by 2
  38. 2021Scatterbrained: A flexible and expandable pattern for decentralized machine learningcited by 2

50 most cited of 85 All 85 (JSON)