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Blockchained On-Device Federated Learning

arXiv 1808.03949 ↗First posted 12 Aug 2018cs.IT cs.NI math.ITcited by 105

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Abstract (arXiv)

By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified. This enables on-device machine learning without any centralized training data or coordination by utilizing a consensus mechanism in blockchain. Moreover, we analyze an end-to-end latency model of BlockFL and characterize the optimal block generation rate by considering communication, computation, and consensus delays.

Key facts. Cited by 105 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Aug 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 34; 2022: 21; 2023: 24; 2024: 10; 2025: 8; 2026: 8.

105 papers.

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  1. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2021Federated Quantum Machine Learningcited by 82
  4. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  5. 2022Mechanisms that Incentivize Data Sharing in Federated Learningcited by 38
  6. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  7. 2023A Survey on Decentralized Federated Learningcited by 27
  8. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  9. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  10. 2022The Cost of Training Machine Learning Models over Distributed Data Sourcescited by 18
  11. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  12. 2021A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guaranteecited by 16
38 more
  1. 2021Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthinesscited by 15
  2. 2021A Systematic Survey of Blockchained Federated Learningcited by 15
  3. 2022Trustworthy Federated Learning via Blockchaincited by 15
  4. 2023IronForge: An Open, Secure, Fair, Decentralized Federated Learningcited by 15
  5. 2021Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensuscited by 14
  6. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
  7. 2022Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computingcited by 13
  8. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  9. 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
  10. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  11. 2022Towards Verifiable Federated Learningcited by 11
  12. 2021Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networkscited by 10
  13. 2021Secure and Efficient Federated Learning Through Layering and Sharding Blockchaincited by 10
  14. 2021Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learningcited by 10
  15. 2023Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Thingscited by 10
  16. 2021OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learningcited by 9
  17. 2022FedToken: Tokenized Incentives for Data Contribution in Federated Learningcited by 9
  18. 2021Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modelingcited by 8
  19. 2021A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Datacited by 8
  20. 2023Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learningcited by 8
  21. 2023Defending Against Poisoning Attacks in Federated Learning with Blockchaincited by 8
  22. 2023Expressive variational quantum circuits provide inherent privacy in federated learningcited by 8
  23. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  24. 2023A Survey of Federated Evaluation in Federated Learningcited by 7
  25. 2024Privacy-Preserving in Blockchain-based Federated Learning Systemscited by 7
  26. 2021Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensingcited by 6
  27. 2022Addressing Heterogeneity in Federated Learning via Distributional Transformationcited by 6
  28. 2021Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligentcited by 5
  29. 2021Blockchain-based Trustworthy Federated Learning Architecturecited by 5
  30. 2021OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Frameworkcited by 5
  31. 2021FLoBC: A Decentralized Blockchain-Based Federated Learning Frameworkcited by 5
  32. 2022Blockchain-based Secure Client Selection in Federated Learningcited by 5
  33. 2022A Fast Blockchain-based Federated Learning Framework with Compressed Communicationscited by 5
  34. 2023Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchaincited by 5
  35. 2023Blockchained Federated Learning for Internet of Things: A Comprehensive Surveycited by 5
  36. 2021Implicit Model Specialization through DAG-based Decentralized Federated Learningcited by 4
  37. 2022FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learningcited by 4
  38. 2022ModularFed: Leveraging Modularity in Federated Learning Frameworkscited by 4

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