mlsanityData to 14 Oct 2026

Backdoor Attacks in Peer-to-Peer Federated Learning

arXiv 2301.09732 ↗First posted 23 Jan 2023cs.LG cs.CRcited by 9

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Abstract (arXiv)

Most machine learning applications rely on centralized learning processes, opening up the risk of exposure of their training datasets. While federated learning (FL) mitigates to some extent these privacy risks, it relies on a trusted aggregation server for training a shared global model. Recently, new distributed learning architectures based on Peer-to-Peer Federated Learning (P2PFL) offer advantages in terms of both privacy and reliability. Still, their resilience to poisoning attacks during training has not been investigated. In this paper, we propose new backdoor attacks for P2PFL that leverage structural graph properties to select the malicious nodes, and achieve high attack success, while remaining stealthy. We evaluate our attacks under various realistic conditions, including multiple graph topologies, limited adversarial visibility of the network, and clients with non-IID data. Finally, we show the limitations of existing defenses adapted from FL and design a new defense that successfully mitigates the backdoor attacks, without an impact on model accuracy.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 59 of its 106 references that are in the map. First posted on arXiv 23 Jan 2023.

Builds on

59 of 106 references.

References · 59

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
  6. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  7. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,106
  8. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,055
  9. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  10. 2018How To Backdoor Federated Learningcited by 765
  11. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  12. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
38 more
  1. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  3. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 519
  4. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 502
  5. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  6. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  7. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  8. 2019A Survey of Deep Learning Techniques for Autonomous Drivingcited by 403
  9. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  10. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  11. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  12. 2018The Hidden Vulnerability of Distributed Learning in Byzantiumcited by 329
  13. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 309
  14. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  15. 2019Can You Really Backdoor Federated Learning?cited by 255
  16. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 225
  17. 2017EMNIST: an extension of MNIST to handwritten letterscited by 213
  18. 2018D2: Decentralized Training over Decentralized Datacited by 140
  19. 2021Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learningcited by 118
  20. 2019Decentralized Deep Learning with Arbitrary Communication Compressioncited by 111
  21. 2018Is feature selection secure against training data poisoning?cited by 93
  22. 2019Peer-to-peer Federated Learning on Graphscited by 85
  23. 2020On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shapingcited by 83
  24. 2018Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attackscited by 81
  25. 2022DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspectioncited by 81
  26. 2016Decentralized Collaborative Learning of Personalized Models over Networkscited by 76
  27. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  28. 2022Fishing for User Data in Large-Batch Federated Learning via Gradient Magnificationcited by 73
  29. 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 64
  30. 2017Personalized and Private Peer-to-Peer Machine Learningcited by 54
  31. 2019BRIDGE: Byzantine-resilient Decentralized Gradient Descentcited by 45
  32. 2017ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learningcited by 36
  33. 2022Byzantine-Robust Decentralized Learning via ClippedGossipcited by 31
  34. 2020Improving on-device speaker verification using federated learning with privacycited by 30
  35. 2016Personalized Speech recognition on mobile devicescited by 23
  36. 2018GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descentcited by 19
  37. 2021Graph Vulnerability and Robustness: A Surveycited by 14
  38. 2018Phocas: dimensional Byzantine-resilient stochastic gradient descentcited by 13

50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 0; 2026: 3.

9 papers.

  1. 2024Byzantine-Robust Decentralized Federated Learningcited by 48
  2. 2023SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspectioncited by 3
  3. 2023Competitive Advantage Attacks to Decentralized Federated Learningcited by 2
  4. 2024Swarm Learning: A Survey of Concepts, Applications, and Trendscited by 2
  5. 2023The Impact of Adversarial Node Placement in Decentralized Federated Learning Networkscited by 1
  6. 2023Mitigating Data Injection Attacks on Federated Learningcited by 1
  7. 2026Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learningcited by 1
  8. 2026Optimizing Byzantine Node Placement in Decentralized Federated Learningcited by 0
  9. 2026Adaptive Bayesian Partner Selection for Federated Clinical Centerscited by 0

Similar, not cited

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  1. 2024Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses
  2. 2026BackDFL: A Unified Benchmark For Backdoor Attacks and Defenses In Decentralized Federated Learning
  3. 2022More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks
  4. 2023On the Vulnerability of Backdoor Defenses for Federated Learning
  5. 2023G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering
  6. 2025Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning
  7. 2024Client-Side Patching against Backdoor Attacks in Federated Learning
  8. 2022Invariant Aggregator for Defending against Federated Backdoor Attacks