mlsanityData to 7 Oct 2026

A Survey on Decentralized Federated Learning

arXiv 2308.04604 ↗First posted 8 Aug 2023cs.LG cs.CR cs.DCcited by 27

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Abstract (arXiv)

Federated learning (FL) enables collaborative training without pooling raw data, but standard FL relies on a central coordinator, which introduces a single point of failure and concentrates trust in the orchestration infrastructure. Decentralized federated learning (DFL) removes the coordinator and replaces client-server orchestration with peer-to-peer coordination, making learning dynamics topology-dependent and reshaping the associated security, privacy, and systems trade-offs. This survey systematically reviews DFL methods from 2018 through early 2026 and organizes them into two architectural families: traditional distributed FL and blockchain-based FL. We then propose a unified, challenge-driven taxonomy that maps both families to the core bottlenecks they primarily address, and we summarize prevailing evaluation practices and their limitations, exposing gaps in the literature. Finally, we distill lessons learned and outline research directions, emphasizing topology-aware threat models, privacy notions that reflect decentralized exposure, incentive mechanisms robust to manipulation, and the need to explicitly define whether the objective is a single global model or personalized solutions in decentralized settings.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 53 of its 156 references that are in the map. First posted on arXiv 8 Aug 2023.

Builds on

53 of 156 references.

References · 53

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Learning with Non-IID Datacited by 1,099
  5. 2019Deep Leakage from Gradientscited by 1,004
  6. 2018How To Backdoor Federated Learningcited by 745
  7. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  8. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  9. 2020iDLG: Improved Deep Leakage from Gradientscited by 367
  10. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  11. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 324
  12. 2021A Field Guide to Federated Optimizationcited by 312
38 more
  1. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  2. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  3. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 285
  4. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 219
  5. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  6. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  7. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  8. 2018ATOMO: Communication-efficient Learning via Atomic Sparsificationcited by 135
  9. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 126
  10. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 119
  11. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
  12. 2018Blockchained On-Device Federated Learningcited by 105
  13. 2018Generalized Byzantine-tolerant SGDcited by 94
  14. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  15. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
  16. 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 81
  17. 2022The Creativity of Text-to-Image Generationcited by 73
  18. 2014IPFS - Content Addressed, Versioned, P2P File Systemcited by 67
  19. 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 63
  20. 2020Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Frameworkcited by 59
  21. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
  22. 2021Decentralized Federated Learning through Proxy Model Sharingcited by 56
  23. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 55
  24. 2017Personalized and Private Peer-to-Peer Machine Learningcited by 54
  25. 2019Practical Federated Gradient Boosting Decision Treescited by 53
  26. 2019Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approachcited by 45
  27. 2020Decentralized Federated Learning via Mutual Knowledge Transfercited by 42
  28. 2020FedCoin: A Peer-to-Peer Payment System for Federated Learningcited by 39
  29. 2019Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithmcited by 26
  30. 2021IPLS : A Framework for Decentralized Federated Learningcited by 26
  31. 2022Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matchingcited by 24
  32. 2021Decentralized federated learning of deep neural networks on non-iid datacited by 23
  33. 2021Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunitiescited by 22
  34. 2019PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networkscited by 18
  35. 2022Trustworthy Federated Learning via Blockchaincited by 15
  36. 2020GFL: A Decentralized Federated Learning Framework Based On Blockchaincited by 12
  37. 2019Blockchain Technology Overviewcited by 11
  38. 2022A Practical Cross-Device Federated Learning Framework over 5G Networkscited by 7

50 most cited of 53 All 53 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 7; 2025: 8; 2026: 6.

27 papers.

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  1. 2023GradientCoin: A Peer-to-Peer Decentralized Large Language Modelscited by 23
  2. 2023Federated Learning for Computer Visioncited by 22
  3. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  4. 2024Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networkscited by 8
  5. 2023DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learningcited by 6
  6. 2024SoK: Blockchain-Based Decentralized AI (DeAI)cited by 6
  7. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  8. 2023Asymmetrically Decentralized Federated Learningcited by 5
  9. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  10. 2023Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environmentscited by 2
  11. 2024Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learningcited by 2
  12. 2024Boosting the Performance of Decentralized Federated Learning via Catalyst Accelerationcited by 2
15 more
  1. 2025Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learningcited by 2
  2. 2024OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancementcited by 1
  3. 2025The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directionscited by 1
  4. 2025Sharp Gaussian approximations for Decentralized Federated Learningcited by 1
  5. 2025Unveiling the Power of Multiple Gossip Steps: A Stability-Based Generalization Analysis in Decentralized Trainingcited by 1
  6. 2023The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistenciescited by 0
  7. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  8. 2025Scalable Asynchronous Federated Modeling for Spatial Datacited by 0
  9. 2025Communication-Efficient Neural Tangent Kernels for Heterogeneous Decentralized Federated Learningcited by 0
  10. 2026Federated Learning and Class Imbalancescited by 0
  11. 2026ERIS: Enhancing Privacy and Scalability in Federated Learning via Federated Shard Aggregationcited by 0
  12. 2026SeedFlood: A Step Toward Scalable Decentralized Fine-Tuning of LLMscited by 0
  13. 2026Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learningcited by 0
  14. 2026Optimizing Byzantine Node Placement in Decentralized Federated Learningcited by 0
  15. 2026Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaborationcited by 0