mlsanityData to 14 Oct 2026

A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing

arXiv 2303.13727 ↗First posted 24 Mar 2023cs.CR cs.DC cs.LGcited by 3

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Abstract (arXiv)

Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users' sensitive data, the performance of the FL process can be threatened and reached a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of FL process, the integration of blockchain for FL environments has drawn prolific attention from the people of academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristic. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms that are suitable for FL process and discuss their trade-offs for a limited resource budget. Further, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role to block those cyber attacks. To this end, we highlight some potential solutions towards the coupling of blockchain and federated learning that can offer high levels of reliability, data privacy, and distributed computing performance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 38 of its 122 references that are in the map. First posted on arXiv 24 Mar 2023.

Builds on

38 of 122 references.

References · 38

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2017Evasion Attacks against Machine Learning at Test Timecited by 689
  6. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  7. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 519
  8. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  9. 2020Label-Only Membership Inference Attackscited by 388
  10. 2020Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networkscited by 349
  11. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  12. 2019MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examplescited by 197
26 more
  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  2. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  3. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 166
  4. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 149
  5. 2018Blockchained On-Device Federated Learningcited by 106
  6. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 76
  7. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
  8. 2021Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocationcited by 56
  9. 2021SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsificationcited by 46
  10. 2019Security and Privacy on Blockchaincited by 32
  11. 2020Federated Transfer Learning: concept and applicationscited by 32
  12. 2020When Federated Learning Meets Blockchain: A New Distributed Learning Paradigmcited by 31
  13. 2021Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AIcited by 26
  14. 2020Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directionscited by 25
  15. 2022Practical Vertical Federated Learning with Unsupervised Representation Learningcited by 19
  16. 2021FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robotscited by 18
  17. 2021Anomaly Detection in Blockchain Networks: A Comprehensive Surveycited by 16
  18. 2019Blockchain for Internet of Things: A Surveycited by 15
  19. 2022Trustworthy Federated Learning via Blockchaincited by 15
  20. 2020Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learningcited by 14
  21. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  22. 2020A Survey on Blockchain Interoperability: Past, Present, and Future Trendscited by 13
  23. 2022A Survey on Participant Selection for Federated Learning in Mobile Networkscited by 11
  24. 2017BLOCKBENCH: A Framework for Analyzing Private Blockchainscited by 5
  25. 2022A Fast Blockchain-based Federated Learning Framework with Compressed Communicationscited by 5
  26. 2020Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approachcited by 4

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 186
  2. 2023A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiencycited by 21
  3. 2025Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniquescited by 0