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Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions (2403.00873v2)

Published 1 Mar 2024 in cs.CR and cs.LG

Abstract: Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into FL systems to provide stronger security, fairness, and scalability. However, blockchain-empowered FL (BC-FL) systems introduce additional demands on network, computing, and storage resources. This survey provides a comprehensive review of recent research on BC-FL systems, analyzing the benefits and challenges associated with blockchain integration. We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration. Additionally, we offer insights on future research directions for the BC-FL system.

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References (130)
  1. Online privacy and informed consent: The dilemma of information asymmetry. Proceedings of the Association for Information Science and Technology, 52(1):1–10, 2015.
  2. Ashok Kumar Reddy Nadikattu. Iot and the issue of data privacy. International Journal of Innovations in Engineering Research and Technology, 5(10):23–26, 2018.
  3. User data privacy: Facebook, cambridge analytica, and privacy protection. Computer, 51(8):56–59, 2018.
  4. Data protection in ai services: A survey. ACM Computing Surveys, 54(2):1–38, 2021.
  5. A survey on data collection for machine learning: a big data-ai integration perspective. IEEE Transactions on Knowledge and Data Engineering, 33(4):1328–1347, 2019.
  6. Data management challenges for deep learning. In 45th Euromicro Conference on Software Engineering and Advanced Applications, pages 140–147. IEEE, 2019.
  7. Deep learning. nature, 521(7553):436–444, 2015.
  8. A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions. ACM Computing Surveys, 54(6):1–36, 2021.
  9. Eu general data protection regulation: Changes and implications for personal data collecting companies. Computer Law & Security Review, 34(1):134–153, 2018.
  10. The eu’s general data protection regulation (gdpr) in a research context. Fundamentals of clinical data science, pages 55–71, 2019.
  11. Shawn Marie Boyne. Data protection in the united states. The American Journal of Comparative Law, 66(suppl_1):299–343, 2018.
  12. Federated learning on non-iid data silos: An experimental study. In IEEE 38th International Conference on Data Engineering, pages 965–978. IEEE, 2022.
  13. Federated learning for smart healthcare: A survey. ACM Computing Surveys, 55(3):1–37, 2022.
  14. A survey of blockchain from the perspectives of applications, challenges, and opportunities. IEEE Access, 7:117134–117151, 2019.
  15. Blockchain for internet of things: A survey. IEEE Internet of Things Journal, 6(5):8076–8094, 2019.
  16. Blockchain practices, potentials, and perspectives in greening supply chains. Sustainability, 10(10):3652, 2018.
  17. A survey on the security of blockchain systems. Future generation computer systems, 107:841–853, 2020.
  18. Martin Von Haller Gronbaek. Blockchain 2.0, smart contracts and challenges. Comput. Law, SCL Mag, 1:1–5, 2016.
  19. Blockchain 2.0: smart contracts. In Advances in Computers, volume 121, pages 301–322. Elsevier, 2021.
  20. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273–1282. PMLR, 2017.
  21. Eden: Communication-efficient and robust distributed mean estimation for federated learning. In International Conference on Machine Learning, pages 21984–22014. PMLR, 2022.
  22. Communication-efficient adaptive federated learning. In International Conference on Machine Learning, pages 22802–22838. PMLR, 2022.
  23. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2):1–210, 2021.
  24. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1–19, 2019.
  25. A secure cloudlet-based charging station recommendation for electric vehicles empowered by federated learning. IEEE Transactions on Industrial Informatics, 18(9):6464–6473, 2022.
  26. A method of federated learning based on blockchain. In Proceedings of the 5th International Conference on Computer Science and Application Engineering, pages 1–8, 2021.
  27. Federated transfer learning for iiot devices with low computing power based on blockchain and edge computing. IEEE Access, 9:98630–98638, 2021.
  28. A survey on federated learning for resource-constrained iot devices. IEEE Internet of Things Journal, 9(1):1–24, 2021.
  29. Federated analytics: Opportunities and challenges. IEEE Network, 36(1):151–158, 2021.
  30. Differential privacy for deep and federated learning: A survey. IEEE access, 10:22359–22380, 2022.
  31. Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6):305–311, 2020.
  32. Blockchain challenges and opportunities: A survey. International journal of web and grid services, 14(4):352–375, 2018.
  33. Core concepts, challenges, and future directions in blockchain: A centralized tutorial. ACM Computing Surveys, 53(1):1–39, 2020.
  34. A survey of blockchain consensus protocols. ACM Computing Surveys, 55(13s), 2023.
  35. A survey on blockchain consensus with a performance comparison of pow, pos and pure pos. Mathematics, 8(10):1782, 2020.
  36. Performance analysis of the raft consensus algorithm for private blockchains. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(1):172–181, 2019.
  37. A scalable multi-layer pbft consensus for blockchain. IEEE Transactions on Parallel and Distributed Systems, 32(5):1146–1160, 2020.
  38. Privacy-preserved cyberattack detection in industrial edge of things (ieot): a blockchain-orchestrated federated learning approach. IEEE Transactions on Industrial Informatics, 18(11):7920–7934, 2022.
  39. A privacy-preserving and verifiable federated learning method based on blockchain. Computer Communications, 186:1–11, 2022.
  40. Blockchain-empowered decentralized horizontal federated learning for 5g-enabled uavs. IEEE Transactions on Industrial Informatics, 18(5):3582–3592, 2021.
  41. Sandbox computing: A data privacy trusted sharing paradigm via blockchain and federated learning. IEEE Transactions on Computers, 72(3):800–810, 2022.
  42. Cooperative federated learning and model update verification in blockchain-empowered digital twin edge networks. IEEE Internet of Things Journal, 9(13):11154–11167, 2021.
  43. Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing. IEEE Transactions on Vehicular Technology, 70(6):6073–6084, 2021.
  44. Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Transactions on Vehicular Technology, 69(4):4298–4311, 2020.
  45. Federated learning for covid-19 detection with generative adversarial networks in edge cloud computing. IEEE Internet of Things Journal, 9(12):10257–10271, 2021.
  46. Latency optimization for blockchain-empowered federated learning in multi-server edge computing. IEEE Journal on Selected Areas in Communications, 40(12):3373–3390, 2022.
  47. High-quality model aggregation for blockchain-based federated learning via reputation-motivated task participation. IEEE Internet of Things Journal, 2022.
  48. A blockchain-enabled federated learning model for privacy preservation: System design. In Australasian Conference on Information Security and Privacy, pages 473–489. Springer, 2021.
  49. A blockchained federated learning framework for cognitive computing in industry 4.0 networks. IEEE Transactions on Industrial Informatics, 17(4):2964–2973, 2020.
  50. Trustfed: A framework for fair and trustworthy cross-device federated learning in iiot. IEEE Transactions on Industrial Informatics, 17(12):8485–8494, 2021.
  51. A blockchain-based multi-layer decentralized framework for robust federated learning. In 2022 International Joint Conference on Neural Networks, pages 1–8, 2022.
  52. Bafl: an efficient blockchain-based asynchronous federated learning framework. In 2021 IEEE Symposium on Computers and Communications, pages 1–6. IEEE, 2021.
  53. Besifl: Blockchain empowered secure and incentive federated learning paradigm in iot. IEEE Internet of Things Journal, 2021.
  54. Bc-edgefl: A defensive transmission model based on blockchain-assisted reinforced federated learning in iiot environment. IEEE Transactions on Industrial Informatics, 18(5):3551–3561, 2021.
  55. Deep-reinforcement-learning-based latency minimization in edge intelligence over vehicular networks. IEEE Internet of Things Journal, 9(2):1300–1312, 2021.
  56. Blockchain assisted decentralized federated learning (blade-fl): Performance analysis and resource allocation. IEEE Transactions on Parallel and Distributed Systems, 33(10):2401–2415, 2021.
  57. Fl-sec: Privacy-preserving decentralized federated learning using signsgd for the internet of artificially intelligent things. IEEE Internet of Things Magazine, 5(1):85–90, 2022.
  58. Cgan-based collaborative intrusion detection for uav networks: A blockchain-empowered distributed federated learning approach. IEEE Internet of Things Journal, 10(1):120–132, 2022.
  59. Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory. IEEE Internet of Things Journal, 6(6):10700–10714, 2019.
  60. Reliable federated learning for mobile networks. IEEE Wireless Communications, 27(2):72–80, 2020.
  61. Optimizing task assignment for reliable blockchain-empowered federated edge learning. IEEE Transactions on Vehicular Technology, 70(2):1910–1923, 2021.
  62. Repbfl: Reputation based blockchain-enabled federated learning framework for data sharing in internet of vehicles. In International Conference on Parallel and Distributed Computing: Applications and Technologies, pages 536–547. Springer, 2022.
  63. FGFL: A blockchain-based fair incentive governor for Federated Learning. Journal of Parallel and Distributed Computing, 163:283–299, 2022.
  64. Federated learning with blockchain approach for trust management in iov. In Proceedings of the 36th International Conference on Advanced Information Networking and Applications, Volume 1, pages 411–423. Springer, 2022.
  65. Rendering secure and trustworthy edge intelligence in 5g-enabled iiot using proof of learning consensus protocol. IEEE Transactions on Industrial Informatics, 19(1):900–909, 2022.
  66. Secure and provenance enhanced internet of health things framework: A blockchain managed federated learning approach. IEEE Access, 8:205071–205087, 2020.
  67. Privacy-preserving blockchain-based federated learning for iot devices. IEEE Internet of Things Journal, 8(3):1817–1829, 2020.
  68. Profit sharing and efficiency in utility games. In 25th Annual European Symposium on Algorithms. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017.
  69. A fairness-aware incentive scheme for federated learning. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pages 393–399, 2020.
  70. A survey of incentive mechanism design for federated learning. IEEE Transactions on Emerging Topics in Computing, 10(2):1035–1044, 2021.
  71. A learning-based incentive mechanism for federated learning. IEEE Internet of Things Journal, 7(7):6360–6368, 2020.
  72. Dim-ds: Dynamic incentive model for data sharing in federated learning based on smart contracts and evolutionary game theory. IEEE Internet of Things Journal, 9(23):24572–24584, 2022.
  73. Byzantine resistant secure blockchained federated learning at the edge. IEEE Network, 35(4):295–301, 2021.
  74. A secure federated learning framework for 5g networks. IEEE Wireless Communications, 27(4):24–31, 2020.
  75. When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm. IEEE Computational Intelligence Magazine, 17(3):26–33, 2022.
  76. Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet of Things Journal, 7(6):5171–5183, 2020.
  77. Fedtwin: Blockchain-enabled adaptive asynchronous federated learning for digital twin networks. IEEE Network, 36(6):183–190, 2022.
  78. Blockchain empowered federated learning for data sharing incentive mechanism. Procedia Computer Science, 202:348–353, 2022.
  79. DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-Based Incentive. IEEE Transactions on Dependable and Secure Computing, 18(5):2438–2455, 2021.
  80. Ronghua Xu and Yu Chen. μ𝜇\muitalic_μdfl: A secure microchained decentralized federated learning fabric atop iot networks. IEEE Transactions on Network and Service Management, 19(3):2677–2688, 2022.
  81. A blockchain-based model migration approach for secure and sustainable federated learning in iot systems. IEEE Internet of Things Journal, 10(8):6574–6585, 2022.
  82. Refiner: A reliable incentive-driven federated learning system powered by blockchain. Proceedings of the VLDB Endowment, 14(12):2659–2662, 2021.
  83. Poster: A reliable and accountable privacy-preserving federated learning framework using the blockchain. In ACM Conference on Computer and Communications Security, pages 2561–2563, 2019.
  84. Blockchain-empowered federated learning approach for an intelligent and reliable d2d caching scheme. IEEE Internet of Things Journal, 9(11):7879–7890, 2021.
  85. Two-layered blockchain architecture for federated learning over the mobile edge network. IEEE Network, 36(1):45–51, 2021.
  86. Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in iiot. IEEE Transactions on Industrial Informatics, 18(6):4049–4058, 2021.
  87. A secure federated transfer learning framework. IEEE Intelligent Systems, 35(4):70–82, 2020.
  88. Blockchain and federated learning for 5g beyond. IEEE Network, 35(1):219–225, 2020.
  89. Privacy-preserving byzantine-robust federated learning via blockchain systems. IEEE Transactions on Information Forensics and Security, 17:2848–2861, 2022.
  90. Fabricfl: Blockchain-in-the-loop federated learning for trusted decentralized systems. IEEE Systems Journal, 16(3):3711–3722, 2021.
  91. Permissioned blockchain frame for secure federated learning. IEEE Communications Letters, 26(1):13–17, 2021.
  92. A survey of blockchain consensus algorithms performance evaluation criteria. Expert Systems with Applications, 154:113385, 2020.
  93. Performance evaluation of blockchain systems: A systematic survey. IEEE Access, 8:126927–126950, 2020.
  94. Toward on-device federated learning: A direct acyclic graph-based blockchain approach. IEEE Transactions on Neural Networks and Learning Systems, 34(4):2028–2042, 2023.
  95. Deep reinforcement learning for resource management in blockchain-enabled federated learning network. IEEE Networking Letters, 4(3):137–141, 2022.
  96. A blockchain-based decentralized federated learning framework with committee consensus. IEEE Network, 35(1):234–241, 2020.
  97. A review on consensus algorithm of blockchain. In IEEE international conference on systems, man, and cybernetics, pages 2567–2572. IEEE, 2017.
  98. Increased block size and bitcoin blockchain dynamics. In International Telecommunication Networks and Applications Conference, pages 1–6. IEEE, 2017.
  99. Improvement of the dpos consensus mechanism in blockchain based on vague sets. IEEE Transactions on Industrial Informatics, 16(6):4252–4259, 2019.
  100. Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 34(6):26–38, 2017.
  101. Dueling network architectures for deep reinforcement learning. In International conference on machine learning, pages 1995–2003. PMLR, 2016.
  102. A self-tuning actor-critic algorithm. Advances in neural information processing systems, 33:20913–20924, 2020.
  103. Towards a trust-enhanced blockchain p2p topology for enabling fast and reliable broadcast. IEEE Transactions on Network and Service Management, 17(2):904–917, 2020.
  104. A survey on the scalability of blockchain systems. IEEE Network, 33(5):166–173, 2019.
  105. An efficient and compacted dag-based blockchain protocol for industrial internet of things. IEEE Transactions on Industrial Informatics, 16(6):4134–4145, 2019.
  106. Double-spending with a sybil attack in the bitcoin decentralized network. IEEE transactions on Industrial Informatics, 15(10):5715–5722, 2019.
  107. Privacy leakage of adversarial training models in federated learning systems. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 108–114, 2022.
  108. Secretgen: Privacy recovery on pre-trained models via distribution discrimination. In European Conference on Computer Vision, pages 139–155. Springer, 2022.
  109. St-bfl: A structured transparency empowered cross-silo federated learning on the blockchain framework. IEEE Access, 9:155634–155650, 2021.
  110. A blockchain-based audit approach for encrypted data in federated learning. Digital Communications and Networks, 8(5):614–624, 2022.
  111. A survey on homomorphic encryption schemes: Theory and implementation. ACM Computing Surveys, 51(4):1–35, 2018.
  112. A privacy-preserving asynchronous averaging algorithm based on shamir’s secret sharing. In 27th European Signal Processing Conference, pages 1–5. IEEE, 2019.
  113. When federated learning meets blockchain: A new distributed learning paradigm. IEEE Computational Intelligence Magazine, 17(3):26–33, 2022.
  114. Deep learning with differential privacy. In ACM Conference on Computer and Communications Security, pages 308–318, 2016.
  115. Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data. In IEEE International Conference on Blockchain, pages 550–555. IEEE, 2020.
  116. Secure multi-party computation: theory, practice and applications. Information Sciences, 476:357–372, 2019.
  117. A redactable blockchain framework for secure federated learning in industrial internet of things. IEEE Internet of Things Journal, 9(18):17901–17911, 2022.
  118. Lightfed: An efficient and secure federated edge learning system on model splitting. IEEE Transactions on Parallel and Distributed Systems, 33(11):2701–2713, 2021.
  119. Trustchain: A sybil-resistant scalable blockchain. Future Generation Computer Systems, 107:770–780, 2020.
  120. Detecting sybil attacks using proofs of work and location in vanets. IEEE Transactions on Dependable and Secure Computing, 19(1):39–53, 2020.
  121. Utilizing public blockchains for the sybil-resistant bootstrapping of distributed anonymity services. In Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, pages 531–542, 2020.
  122. Biscotti: A blockchain system for private and secure federated learning. IEEE Transactions on Parallel and Distributed Systems, 32(7):1513–1525, 2020.
  123. Algorand: Scaling byzantine agreements for cryptocurrencies. In Proceedings of the 26th symposium on operating systems principles, pages 51–68, 2017.
  124. Identifying impacts of protocol and internet development on the bitcoin network. In IEEE Symposium on Computers and Communications, pages 1–6. IEEE, 2020.
  125. Baffle: Blockchain based aggregator free federated learning. In 2020 IEEE international conference on blockchain, pages 72–81. IEEE, 2020.
  126. Federated learning-based secure electronic health record sharing scheme in medical informatics. IEEE Journal of Biomedical and Health Informatics, 2022.
  127. Learning markets: An ai collaboration framework based on blockchain and smart contracts. IEEE Internet of Things Journal, 9(16):14273–14286, 2020.
  128. Smart contract security: a practitioners’ perspective. In IEEE/ACM 43rd International Conference on Software Engineering, pages 1410–1422. IEEE, 2021.
  129. Verismart: A highly precise safety verifier for ethereum smart contracts. In IEEE Symposium on Security and Privacy, pages 1678–1694. IEEE, 2020.
  130. Verx: Safety verification of smart contracts. In IEEE symposium on security and privacy, pages 1661–1677. IEEE, 2020.
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