mlsanityData to 7 Oct 2026

FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks

arXiv 2411.02773 ↗First posted 5 Nov 2024cs.CR cs.CVcited by 2

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Abstract (arXiv)

Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security risks. First, because FL depends on a central server to aggregate local training models, this is a single point of failure. The server might function maliciously. Second, due to its distributed nature, FL might encounter backdoor attacks by participating clients. They can poison the local model before submitting to the server. Either type of attack, on the server or the client side, would severely degrade learning accuracy. We propose FedBlock, a novel blockchain-based FL framework that addresses both of these security risks. FedBlock is uniquely desirable in that it involves only smart contract programming, thus deployable atop any blockchain network. Our framework is substantiated with a comprehensive evaluation study using real-world datasets. Its robustness against backdoor attacks is competitive with the literature of FL backdoor defense. The latter, however, does not address the server risk as we do.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 32 references that are in the map. First posted on arXiv 5 Nov 2024.

Builds on

20 of 32 references.

References · 20

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018How To Backdoor Federated Learningcited by 745
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  6. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  7. 2019Robust Aggregation for Federated Learningcited by 314
  8. 2020Attack of the Tails: Yes, You Really Can Backdoor Federated Learningcited by 307
  9. 2019Can You Really Backdoor Federated Learning?cited by 250
  10. 2022FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clientscited by 119
  11. 2021CRFL: Certifiably Robust Federated Learning against Backdoor Attackscited by 91
  12. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
8 more
  1. 2022Federated Unlearning with Knowledge Distillationcited by 70
  2. 2020Mitigating Backdoor Attacks in Federated Learningcited by 39
  3. 2022FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learningcited by 37
  4. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  5. 2023BayBFed: Bayesian Backdoor Defense for Federated Learningcited by 23
  6. 2022Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesiscited by 12
  7. 2022ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learningcited by 4
  8. 2023FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspectioncited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025RobustFSM: Submodular Maximization in Federated Setting with Malicious Clientscited by 0
  2. 2026FL-PBM: Pre-Training Backdoor Mitigation for Federated Learningcited by 0