mlsanityData to 13 Oct 2026

Decentralized Federated Learning: A Survey on Security and Privacy

arXiv 2401.17319 ↗First posted 25 Jan 2024cs.CR cs.AI cs.LGcited by 25

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Abstract (arXiv)

Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new attack surfaces for malicious users of the network which may jeopardize the model performance and user and data privacy. For this reason, one of the main motivations for decentralized federated learning is to eliminate server-related threats by removing the server from the network and compensating for it through technologies such as blockchain. However, this advantage comes at the cost of challenging the system with new privacy threats. Thus, performing a thorough security analysis in this new paradigm is necessary. This survey studies possible variations of threats and adversaries in decentralized federated learning and overviews the potential defense mechanisms. Trustability and verifiability of decentralized federated learning are also considered in this study.

Key facts. Cited by 25 papers in the map (data through 13 Oct 2026). Builds on 77 of its 166 references that are in the map. First posted on arXiv 25 Jan 2024.

Builds on

77 of 166 references.

References · 77

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,093
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  3. 2019Advances and Open Problems in Federated Learningcited by 3,040
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,981
  5. 2016Deep Learning with Differential Privacycited by 2,718
  6. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,273
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  8. 2019Deep Leakage from Gradientscited by 1,017
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  10. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 563
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  12. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 500
38 more
  1. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 497
  2. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  3. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 456
  4. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  5. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 347
  6. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
  7. 2021Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networkscited by 323
  8. 2019Robust Aggregation for Federated Learningcited by 316
  9. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 308
  10. 2018Certified Defenses against Adversarial Examplescited by 305
  11. 2018Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergencescited by 240
  12. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 224
  13. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 221
  14. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  15. 2020Threats to Federated Learning: A Surveycited by 178
  16. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  17. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  18. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 165
  19. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 162
  20. 2018cpSGD: Communication-efficient and differentially-private distributed SGDcited by 158
  21. 2017Revisiting Distributed Synchronous SGDcited by 142
  22. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 117
  23. 2019Deep Learning with Gaussian Differential Privacycited by 113
  24. 2020Certified Robustness to Label-Flipping Attacks via Randomized Smoothingcited by 108
  25. 2018Blockchained On-Device Federated Learningcited by 106
  26. 2018Privacy Amplification by Iterationcited by 98
  27. 2021PPFL: Privacy-preserving Federated Learning with Trusted Execution Environmentscited by 93
  28. 2019Reliable Federated Learning for Mobile Networkscited by 92
  29. 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
  30. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 88
  31. 2019Peer-to-peer Federated Learning on Graphscited by 85
  32. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 84
  33. 2019Data Poisoning against Differentially-Private Learners: Attacks and Defensescited by 78
  34. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 76
  35. 2016Decentralized Collaborative Learning of Personalized Models over Networkscited by 74
  36. 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 64
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  38. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 56

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Citing papers per year: 2024: 8; 2025: 13; 2026: 4.

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  1. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 15
  2. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  3. 2025Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noisecited by 8
  4. 2024Memory Backdoor Attacks on Neural Networkscited by 3
  5. 2024Swarm Learning: A Survey of Concepts, Applications, and Trendscited by 2
  6. 2024Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning Approachcited by 2
  7. 2025DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Modelscited by 2
  8. 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directionscited by 2
  9. 2024Noise-Robust and Resource-Efficient ADMM-based Federated Learningcited by 1
  10. 2024Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimizationcited by 1
  11. 2025DBA-DFL: Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learningcited by 1
  12. 2025TrustChain: A Blockchain Framework for Auditing and Verifying Aggregators in Decentralized Federated Learningcited by 1
13 more
  1. 2025Fedivertex: a Graph Dataset based on Decentralized Social Networks for Trustworthy Machine Learningcited by 1
  2. 2025RepuNet: A Reputation System for Mitigating Malicious Clients in DFLcited by 1
  3. 2026Decentralized Federated Learning by Partial Message Exchangecited by 1
  4. 2024Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacycited by 0
  5. 2024De-DSI: Decentralised Differentiable Search Indexcited by 0
  6. 2025FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learningcited by 0
  7. 2025Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruningcited by 0
  8. 2025CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detectioncited by 0
  9. 2025Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learningcited by 0
  10. 2025Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptationcited by 0
  11. 2026Resilient Federated Chain: Transforming Blockchain Consensus into an Active Defense Layer for Federated Learningcited by 0
  12. 2026Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizerscited by 0
  13. 2026Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theorycited by 0