[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

A Study of Blockchain-Based Federated Learning

  • Chapter
  • First Online:
Federated and Transfer Learning

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 27))

Abstract

Federated Learning (FL) has made an essential step towards enhancing the privacy of traditional model training. However, gaps in the conventional FL framework make it vulnerable. FL is dealing with a double-edged sword by following the data minimization principle. Although FL provides privacy by design, it makes data verification challenging as no one can see others’ data. Therefore, participants may act dishonestly, increasing the risk of information leakage or performance degradation. It also lacks an incentive mechanism. Most recent studies leveraged blockchain technology to deal with privacy and security problems and address centralization and fairness issues. This study provides a comprehensive literature review on blockchain-based FL systems. Research and applications are presented, and future research directions are offered.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 103.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 129.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
GBP 129.99
Price includes VAT (United Kingdom)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    “Diabetic foot ulcer is a major complication of diabetes mellitus, and probably the major component of the diabetic foot”. (wikipedia).

References

  1. McMahan B, Ramage D (2017) Federated Learning: Collaborative Machine Learning without Centralized Training Data. Google Research Blog 3

    Google Scholar 

  2. Nasr M, Shokri R, Houmansadr A (2019) Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In: 2019 IEEE symposium on security and privacy (SP), pp 739–753

    Google Scholar 

  3. Melis L, Song C, De Cristofaro E, Shmatikov V (2019) Exploiting unintended feature leakage in collaborative learning. In: 2019 IEEE symposium on security and privacy (SP), pp 691–706

    Google Scholar 

  4. Orekondy T, Oh SJ, Zhang Y et al (2018) Gradient-leaks: Understanding and controlling deanonymization in federated learning. arXiv:1805.05838

  5. Ma C, Li J, Ding M et al (2020) On safeguarding privacy and security in the framework of federated learning. IEEE Netw 34:242–248

    Article  Google Scholar 

  6. Wei W, Liu L, Loper M et al (2020) A framework for evaluating gradient leakage attacks in federated learning. arXiv:2004.10397

  7. Kairouz P, McMahan HB, Avent B et al (2019) Advances and open problems in federated learning. arXiv:1912.04977

  8. McMahan B, Moore E, Ramage D et al (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp 1273–1282

    Google Scholar 

  9. Bonawitz K, Ivanov V, Kreuter B et al (2017) Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pp 1175–1191

    Google Scholar 

  10. Wang S, Tuor T, Salonidis T et al (2019) Adaptive federated learning in resource constrained edge computing systems. IEEE J Sel Areas Commun 37:1205–1221

    Article  Google Scholar 

  11. Nishio T, Yonetani R (2019) Client selection for federated learning with heterogeneous resources in mobile edge. In: ICC 2019-2019 IEEE international conference on communications (ICC), pp 1–7

    Google Scholar 

  12. Kang J, Xiong Z, Niyato D et al (2019) Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J 6:10700–10714

    Article  Google Scholar 

  13. Kim H, Park J, Bennis M, Kim SL (2019) Blockchained on-device federated learning. IEEE Commun Lett 24:1279–1283

    Article  Google Scholar 

  14. Qu Y, Gao L, Luan TH et al (2020) Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet Things J 7:5171–5183

    Article  Google Scholar 

  15. Lu Y, Huang X, Dai Y et al (2019) Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Trans Ind Inf 16:4177–4186

    Article  Google Scholar 

  16. Aono Y, Hayashi T, Wang L, Moriai S (2017) Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans Inf Forensics Secur 13:1333–1345

    Google Scholar 

  17. Zhang C, Xie Y, Bai H et al (2021) A survey on federated learning. Knowl-Based Syst 216:106775

    Article  Google Scholar 

  18. Liang G, and Chawathe S (2004) Privacy-preserving inter-database operations. In: International conference on intelligence and security informatics. Springer, pp 66–82

    Google Scholar 

  19. Scannapieco M, Figotin I et al (2007) Privacy preserving schema and data matching. In: Proceedings of the 2007 ACM SIGMOD international conference on management of data, pp 653-664

    Google Scholar 

  20. Wei K, Li J et al (2022) Vertical federated learning: challenges, methodologies and experiments. arXiv:2202.04309

  21. Liu Y, Zhang X, Wang L (2020) Asymmetrical vertical federated learning. arXiv:2004.07427

  22. Chen T, Jin X et al (2020) Vafl: a method of vertical asynchronous federated learning. arXiv:2007.06081

  23. Wu Z, Li Q, He B (2021) Exploiting record similarity for practical vertical federated learning. arXiv:2106.06312

  24. Feng S, Yu H (2020) Multi-participant multi-class vertical federated learning. arXiv:2001.11154

  25. Huang Y, Feng X et al (2022) EFMVFL: an efficient and flexible multi-party vertical federated learning without a third party. arXiv:2201.06244

  26. Mugunthan V, Goyal P, Kagal L (2021) Multi-vfl: a vertical federated learning system for multiple data and label owners. arXiv:2106.05468

  27. Fan Z, Fang H et al (2022) Fair and efficient contribution valuation for vertical federated learning. arXiv:2201.02658

  28. Han X, Wang L, Wu J (2021) Data valuation for vertical federated learning: an information-theoretic approach. arXiv:2112.08364

  29. Li M, Chen Y et al (2020) Efficient asynchronous vertical federated learning via gradient prediction and double-end sparse compression. In: 2020 16th international conference on control, automation, robotics and vision (ICARCV), pp 291–296

    Google Scholar 

  30. Niu S, Liu Y, Wang J, Song H (2020) A decade survey of transfer learning (2010-2020). IEEE Trans Artif Intell 2:151–66

    Google Scholar 

  31. Liu Y, Kang Y et al (2020) A secure federated transfer learning framework. IEEE Intell Syst 4:70–82

    Article  Google Scholar 

  32. Alzubaidi L, Al-Amidie M et al (2021) Novel transfer learning approach for medical imaging with limited labeled data. Cancers 7:1590

    Article  Google Scholar 

  33. Fan Y, Li Y et al (2020) Iotdefender: a federated transfer learning intrusion detection framework for 5g iot. In: 2020 IEEE 14th international conference on big data science and engineering (BigDataSE), pp 88–95

    Google Scholar 

  34. Otoum Y, Wan Y, Nayak A (2021) Federated transfer learning-based ids for the internet of medical things (IoMT). In: 2021 IEEE Globecom workshops (GC Wkshps), pp 1–6

    Google Scholar 

  35. Chen Y, Qin X et al (2020) Fedhealth: a federated transfer learning framework for wearable healthcare. IEEE Intell Syst 4:83–93

    Article  Google Scholar 

  36. Li S, Cai T, Duan R (2021) Targeting underrepresented populations in precision medicine: a federated transfer learning approach. arXiv:2108.12112

  37. Gao D, Liu Y et al (2019) Privacy-preserving heterogeneous federated transfer learning. In: 2019 IEEE international conference on big data (Big Data), pp 2552–2559

    Google Scholar 

  38. Sharma S, Xing C et al (2019) Secure and efficient federated transfer learning. In: 2019 IEEE international conference on big data (Big Data), pp 2569–2576

    Google Scholar 

  39. Jing Q, Wang W et al (2019) Quantifying the performance of federated transfer learning. arXiv:1912.12795

  40. Liu Y, Kang Y et al (2020) A secure federated transfer learning framework. IEEE Intell Syst 4:70–82

    Article  Google Scholar 

  41. Roy AG, Siddiqui S, Pölsterl S et al (2019) Braintorrent: a peer-to-peer environment for decentralized federated learning. arXiv:1905.06731

  42. Korkmaz C, Kocas HE, Uysal A et al (2020) Chain fl: Decentralized federated machine learning via blockchain. In: 2020 second international conference on blockchain computing and applications (BCCA) , pp 140–146

    Google Scholar 

  43. Lalitha A, Shekhar S, Javidi T, Koushanfar F (2018) Fully decentralized federated learning. In: Third workshop on bayesian deep learning (NeurIPS)

    Google Scholar 

  44. Lian X, Zhang C, Zhang H et al (2017) Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent. arXiv:1705.09056

  45. De Filippi P (2016) The interplay between decentralization and privacy: the case of blockchain technologies. J Peer Prod

    Google Scholar 

  46. Xu G, Li H, Liu S et al (2019) Verifynet: Secure and verifiable federated learning. IEEE Trans Inf Forensics Secur 15:911–26

    Article  Google Scholar 

  47. Ma C, Li J, Ding M et al (2020) When federated learning meets blockchain: a new distributed learning paradigm. arXiv:2009.09338

  48. Agrawal N, Shahin Shamsabadi A, Kusner MJ, Gascón A (2019) QUOTIENT: two-party secure neural network training and prediction. In: Proceedings of the 2019 ACM SIGSAC conference on computer and communications security, pp 1231–1247

    Google Scholar 

  49. Dalskov A, Escudero D, Keller M (2019) Secure evaluation of quantized neural networks. arXiv:1910.12435

  50. Nikolaenko V, Weinsberg U, Ioannidis S et al (2013) Privacy-preserving ridge regression on hundreds of millions of records. In: 2013 IEEE symposium on security and privacy, pp 334–348

    Google Scholar 

  51. Mohassel P, Zhang Y (2017) Secureml: a system for scalable privacy-preserving machine learning. In: 2017 IEEE symposium on security and privacy (SP), pp 19–38

    Google Scholar 

  52. Hardy S, Henecka W, Ivey-Law H et al (2017) Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv:1711.10677

  53. Chai D, Wang L, Chen K, Yang Q (2020) Secure federated matrix factorization. IEEE Intell Syst

    Google Scholar 

  54. Pihur V, Korolova A, Liu F et al (2018) Differentially-private “draw and discard” machine learning. arXiv:1807.04369

  55. Shokri R, Stronati M, Song C, Shmatikov V (2017) Membership inference attacks against machine learning models. In: 2017 IEEE symposium on security and privacy (SP), pp 3–18

    Google Scholar 

  56. Bittau A, Erlingsson Ú, Maniatis P et al (2017) Prochlo: strong privacy for analytics in the crowd. In: Proceedings of the 26th symposium on operating systems principles, pp 441–459

    Google Scholar 

  57. Shen J, Zhou T, He D et al (2017) Block design-based key agreement for group data sharing in cloud computing. IEEE Trans Dependable Sec Comput 16:996–1010

    Article  Google Scholar 

  58. Shayan M, Fung C, Yoon CJM, Beschastnikh I (2021) Biscotti: a blockchain system for private and secure federated learning. IEEE Trans Parallel Distrib Syst 32:1513–1525

    Article  Google Scholar 

  59. Shafahi A, Huang WR, Najibi M et al (2018) Poison frogs! targeted clean-label poisoning attacks on neural networks. arXiv:1804.00792

  60. Chen X, Liu C, Li B et al (2017) Targeted backdoor attacks on deep learning systems using data poisoning. arXiv:1712.05526

  61. Douceur JR (2002) The sybil attack. In: International workshop on peer-to-peer systems. Springer, Berlin, Heidelberg

    Google Scholar 

  62. Fung C, Yoon CJ, Beschastnikh I (2018) Mitigating sybils in federated learning poisoning. arXiv:1808.04866

  63. Li T, Sahu AK, Zaheer M et al (2018) Federated optimization in heterogeneous networks. arXiv:1812.06127

  64. Bonawitz K, Eichner H, Grieskamp W et al (2019) Towards federated learning at scale: system design. arXiv:1902.01046

  65. Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag 37:50–60

    Google Scholar 

  66. Karimireddy SP, Kale S, Mohri M et al (2020) Scaffold: Stochastic controlled averaging for federated learning. In: International conference on machine learning, pp 5132–5143

    Google Scholar 

  67. Li Q, Diao Y, Chen Q, He B (2021) Federated learning on non-iid data silos: an experimental study. arXiv:2102.02079

  68. Zheng Z, Xie S, Dai HN et al (2018) Blockchain challenges and opportunities: a survey. Int J Web Grid Serv 14:352–375

    Article  Google Scholar 

  69. Xiao Y, Zhang N, Lou W, Hou YT (2020) A survey of distributed consensus protocols for blockchain networks. IEEE Commun Surv Tutor 22:1432–1465

    Article  Google Scholar 

  70. Wang M, Duan M, Zhu J (2018) Research on the security criteria of hash functions in the blockchain. In: Proceedings of the 2nd ACM workshop on blockchains, cryptocurrencies, and contracts, pp 47–55

    Google Scholar 

  71. Sharma PK, Park JH, Cho K (2020) Blockchain and federated learning-based distributed computing defence framework for sustainable society. Sustain Urban Areas 59:102220

    Google Scholar 

  72. Peng Z, Xu J, Chu X et al (2021) Vfchain: enabling verifiable and auditable federated learning via blockchain systems. IEEE Trans Netw Sci Eng

    Google Scholar 

  73. Zeng R, Zeng C, Wang X et al (2021) A comprehensive survey of incentive mechanism for federated learning. arXiv:2106.15406

  74. Zhan Y, Zhang J, Hong Z et al (2021) A survey of incentive mechanism design for federated learning. IEEE Trans Emerg Top Comput

    Google Scholar 

  75. Desai HB, Ozdayi MS, Kantarcioglu M (2021) Blockfla: accountable federated learning via hybrid blockchain architecture. In: Proceedings of the eleventh ACM conference on data and application security and privacy, pp 101–112

    Google Scholar 

  76. Tran NH, Bao W, Zomaya A et al (2019) Federated learning over wireless networks: Optimization model design and analysis. In: IEEE INFOCOM 2019-IEEE conference on computer communications , pp 1387–1395

    Google Scholar 

  77. Hitaj B, Ateniese G, Perez-Cruz F (2017) Deep models under the GAN: information leakage from collaborative deep learning. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pp 603–618

    Google Scholar 

  78. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur Gener Comput Syst 28:755–68

    Article  Google Scholar 

  79. Kasyap H, Tripathy S (2021) Privacy-preserving decentralized learning framework for healthcare system. ACM Trans Multimed Comput Commun Appl (TOMM) 17:1–24

    Google Scholar 

  80. Zhang H, Li G, Zhang Y et al (2021) Blockchain-based privacy-preserving medical data sharing scheme using federated learning. In: International conference on knowledge science, engineering and management. Springer, Cham, pp 634–646

    Google Scholar 

  81. Lu Y, Huang X, Zhang K et al (2020) Blockchain and federated learning for 5G beyond. IEEE Netw 35:219–225

    Article  Google Scholar 

  82. Liu Y, Qu Y, Xu C (2021) Blockchain-enabled asynchronous federated learning in edge computing. Sensors 21:3335

    Article  Google Scholar 

  83. Chen Y, Chen Q, Xie Y (2020) A methodology for high-efficient federated-learning with consortium blockchain. In: 2020 IEEE 4th conference on energy internet and energy system integration (EI2), pp 3090–3095

    Google Scholar 

  84. Li J, Shao Y, Wei K et al (2021) Blockchain assisted decentralized federated learning (BLADE-FL): performance analysis and resource allocation. CoRR abs/2101.06905:

    Google Scholar 

  85. Peng Y, Chen Z, Chen Z et al (2021) BFLP: an adaptive federated learning framework for internet of vehicles. Mobile Inf Syst

    Google Scholar 

  86. Biham E, Shamir A (1991) Differential cryptanalysis of DES-like cryptosystems. J Cryptol 4:3–72

    Article  MathSciNet  MATH  Google Scholar 

  87. Yin L, Feng J, Lin S et al (2021) A blockchain-based collaborative training method for multi-party data sharing. Comput Commun 173:70–78

    Article  Google Scholar 

  88. Gai K, Wu Y, Zhu L et al (2019) Permissioned blockchain and edge computing empowered privacy-preserving smart grid networks. IEEE Internet Things J 6:7992–8004

    Article  Google Scholar 

  89. Hu Y, Zhou Y, Xiao J, Wu C (2020) GFL: a decentralized federated learning framework based on blockchain. arXiv:2010.10996

  90. Chen H, Asif SA, Park J et al (2021) Robust blockchained federated learning with model validation and proof-of-stake inspired consensus. arXiv:2101.03300

  91. Jiang C, Xu C, Zhang Y (2021) PFLM: privacy-preserving federated learning with membership proof. Inf Sci 576:288–311

    Article  MathSciNet  Google Scholar 

  92. Zeng R, Zhang S, Wang J, Chu X (2020) Fmore: an incentive scheme of multi-dimensional auction for federated learning in mec. In: 2020 IEEE 40th international conference on distributed computing systems (ICDCS), pp 278–288

    Google Scholar 

  93. Lim WYB, Xiong Z, Miao C et al (2020) Hierarchical incentive mechanism design for federated machine learning in mobile networks. IEEE Internet Things J 7:9575–9588

    Article  Google Scholar 

  94. Weng J, Weng J, Zhang J et al (2019) DeepChain: auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Trans Dependable Sec Comput 1–1

    Google Scholar 

  95. Zhan Y, Zhang J, Li P, Xia Y (2019) Crowdtraining: architecture and incentive mechanism for deep learning training in the internet of things. IEEE Netw 33:89–95

    Article  Google Scholar 

  96. Zhan Y, Li P, Qu Z et al (2020) A learning-based incentive mechanism for federated learning. IEEE Internet Things J 7:6360–6368

    Article  Google Scholar 

  97. Zhan Y, Li P, Wang K et al (2020) Big data analytics by crowdlearning: architecture and mechanism design. IEEE Netw 34:143–147

    Article  Google Scholar 

  98. Bao X, Su C, Xiong Y et al (2019) FLChain: a blockchain for auditable federated learning with trust and incentive. In: Proceedings - 5th international conference on big data computing and communications, BIGCOM 2019. Institute of Electrical and Electronics Engineers Inc., pp 151–159

    Google Scholar 

  99. Yi Ming W, Ge Hao L, Li Yu F, Mao P (2021) Research on block chain defense against malicious attack in federated learning. In: 2021 the 3rd international conference on blockchain technology. Association for Computing Machinery, pp 67–72

    Google Scholar 

  100. Jia R, Dao D, Wang B et al (2019) Towards efficient data valuation based on the shapley value

    Google Scholar 

  101. Wang G, Dang CX, Zhou Z (2019) Measure contribution of participants in federated learning. In: 2019 IEEE international conference on big data (Big Data), pp 2597–2604

    Google Scholar 

  102. Zhang W, Lu Q, Yu Q et al (2021) Blockchain-based federated learning for device failure detection in industrial IoT. IEEE Internet Things J 8:5926–5937

    Article  Google Scholar 

  103. Ouyang L, Yuan Y, Cao Y, Wang F-Y (2021) A novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts. Inf Sci 570:124–143

    Article  MathSciNet  Google Scholar 

  104. Xuan S, Jin M, Li X et al (2021) DAM-SE: a blockchain-based optimized solution for the counterattacks in the internet of federated learning systems. Sec Commun Netw 2021:9965157

    Google Scholar 

  105. Li Y, Chen C, Liu N et al (2021) A blockchain-based decentralized federated learning framework with committee consensus. IEEE Netw 35:234–241

    Article  Google Scholar 

  106. Kang J, Xiong Z, Niyato D et al (2020) Reliable federated learning for mobile networks. IEEE Wirel Commun 27:72–80

    Article  Google Scholar 

  107. Feng L, Yang Z, Guo S et al (2021) Two-layered blockchain architecture for federated learning over mobile edge network. IEEE Netw

    Google Scholar 

  108. Zhang Q, Ding Q, Zhu J, Li D (2021) Blockchain empowered reliable federated learning by worker selection: a trustworthy reputation evaluation method. In: 2021 IEEE wireless communications and networking conference workshops (WCNCW), pp 1–6

    Google Scholar 

  109. Liu Y, Ai Z et al (2020) Fedcoin: a peer-to-peer payment system for federated learning. In: Federated learning 2020. Springer, Cham, pp 125–138

    Google Scholar 

  110. Cai H, Rueckert D, Passerat-Palmbach J (2020) 2CP: decentralized protocols to transparently evaluate contributivity in blockchain federated learning environments. CoRR abs/2011.07516

    Google Scholar 

  111. Wang R, Li H, Liu E (2021) Blockchain-based federated learning in mobile edge networks with application in internet of vehicles. CoRR abs/2103.01116

    Google Scholar 

  112. Toyoda K, Zhang AN (2019) Mechanism design for an incentive-aware blockchain-enabled federated learning platform. In: 2019 IEEE international conference on big data (Big Data), pp 395–403

    Google Scholar 

  113. Jiang S, Wu J (2022) A reward response game in the blockchain-powered federated learning system. Int J Parallel Emergent Distrib Syst 37:68–90

    Article  Google Scholar 

  114. Martinez I, Francis S, Hafid AS (2019) Record and reward federated learning contributions with blockchain. In: 2019 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC), pp 50–57

    Google Scholar 

  115. Preuveneers D, Rimmer V et al (2018) Chained anomaly detection models for federated learning: an intrusion detection case study. Appl Sci 8:2663

    Article  Google Scholar 

  116. Hei X, Yin X et al (2020) A trusted feature aggregator federated learning for distributed malicious attack detection. Comput Sec 99:102033

    Article  Google Scholar 

  117. Abdel-Basset M, Moustafa N et al (2021) Federated intrusion detection in blockchain-based smart transportation systems. IEEE Trans Intell Transp Syst

    Google Scholar 

  118. Połap D, Srivastava G, Yu K (2021) Agent architecture of an intelligent medical system based on federated learning and blockchain technology. J Inf Sec Appl 58:102748

    Google Scholar 

  119. Awan S, Li F et al (2019) Poster: a reliable and accountable privacy-preserving federated learning framework using the blockchain. In: Proceedings of the 2019 ACM SIGSAC conference on computer and communications security, pp 2561–2563

    Google Scholar 

  120. El Rifai O, Biotteau M et al (2020) Blockchain-based federated learning in medicine. In: International conference on artificial intelligence in medicine, pp 214–224

    Google Scholar 

  121. Chang Y, Fang C, Sun W (2021) A blockchain-based federated learning method for smart healthcare. Comput Intell Neurosci

    Google Scholar 

  122. Ouyang L, Yuan Y et al (2021) A novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts. Inf Sci 570:124–43

    Article  MathSciNet  Google Scholar 

  123. Kumar R, Khan AA et al (2021) Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging. IEEE Sens J 21:16301–14

    Article  Google Scholar 

  124. Nguyen DC, Ding M et al (2021) Federated learning for covid-19 detection with generative adversarial networks in edge cloud computing. IEEE Internet of Things Journal

    Google Scholar 

  125. Zhang Q, Palacharla P, Sekiya M et al (2021) Blockchain-based secure aggregation for federated learning with a traffic prediction use case. In: 2021 IEEE 7th international conference on network softwarization (NetSoft), pp 372–374

    Google Scholar 

  126. Wang K, Chen CM et al (2021) A trusted consensus fusion scheme for decentralized collaborated learning in massive IoT domain. Inf Fusion 72:100–9

    Article  Google Scholar 

  127. Qu G, Cui N et al (2021) Chainfl: a simulation platform for joint federated learning and blockchain in edge/cloud computing environments. IEEE Trans Ind Inf

    Google Scholar 

  128. Jin H, Dai X et al (2021) Cross-cluster federated learning and blockchain for internet of medical things. IEEE Internet Things J 8:15776–15784

    Article  Google Scholar 

  129. Singh S, Rathore S et al (2022) A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology. Futur Gener Comput Syst 129:380–388

    Article  Google Scholar 

  130. Samuel O, Omojo AB et al (2022) IoMT: a COVID-19 healthcare system driven by federated learning and blockchain. IEEE J Biomed Health Inf

    Google Scholar 

  131. Chai H, Leng S et al (2020) A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles. IEEE Trans Intell Transp Syst 22:3975–3986

    Article  Google Scholar 

  132. Zou Y, Shen F et al (2021) Reputation-based regional federated learning for knowledge trading in blockchain-enhanced IoV. In: 2021 IEEE wireless communications and networking conference (WCNC), pp 1–6

    Google Scholar 

  133. Ghimire B, Rawat DB (2021) Secure, privacy preserving and verifiable federating learning using blockchain for internet of vehicles. IEEE Consum Electron Mag

    Google Scholar 

  134. Liu C, Guo S et al (2021) LTSM: Lightweight and trusted sharing mechanism of IoT data in smart city. IEEE Internet of Things J

    Google Scholar 

  135. Sharma PK, Park JH, Cho K (2020) Blockchain and federated learning-based distributed computing defence framework for sustainable society. Sustain Urban Areas 59:102220

    Google Scholar 

  136. Qin Z, Ye J et al (2021) Privacy-preserving blockchain-based federated learning for marine internet of things. IEEE Trans Comput Soc Syst

    Google Scholar 

  137. Yin B, Yin H et al (2020) FDC: a secure federated deep learning mechanism for data collaborations in the Internet of Things. IEEE Internet Things J 7:6348–6359

    Article  Google Scholar 

  138. Rahman MA, Hossain MS et al (2020) Secure and provenance enhanced internet of health things framework: a blockchain managed federated learning approach. IEEE Access 8:205071–205087

    Article  Google Scholar 

  139. ur Rehman MH, Dirir AM, et al (2021) TrustFed: a framework for fair and trustworthy cross-device federated learning in IIoT. IEEE Trans Ind Inf 17:8485–8494

    Google Scholar 

  140. Jia B, Zhang X et al (2021) Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in IIoT. IEEE Trans Ind Inf

    Google Scholar 

  141. Qi Y, Hossain MS et al (2021) Privacy-preserving blockchain-based federated learning for traffic flow prediction. Futur Gener Comput Syst 117:328–337

    Article  Google Scholar 

  142. Lu Y, Huang X et al (2020) Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans Veh Technol 69:4298–311

    Article  Google Scholar 

  143. Cui L, Qu Y, Xie G et al (2021) Security and privacy-enhanced federated learning for anomaly detection in IoT infrastructures. IEEE Trans Ind Inf

    Google Scholar 

  144. Liu H, Zhang S, Zhang P et al (2021) Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing. IEEE Trans Veh Technol

    Google Scholar 

  145. Kong Q, Yin F, Xiao Y et al (2021) Achieving blockchain-based privacy-preserving location proofs under federated learning. In: ICC 2021 - IEEE international conference on communications, pp 1–6

    Google Scholar 

  146. Doku R, Rawat DB, Liu C (2019) Towards federated learning approach to determine data relevance in big data. In: 2019 IEEE 20th international conference on information reuse and integration for data science (IRI), pp 184–192

    Google Scholar 

  147. Cheng R, Sun Y et al (2021) Blockchain-empowered federated learning approach for an intelligent and reliable D2D caching scheme. IEEE Internet of Things J

    Google Scholar 

  148. Fan S, Zhang H et al (2020) Hybrid blockchain-based resource trading system for federated learning in edge computing. IEEE Internet Things J 8:2252–2264

    Article  Google Scholar 

  149. Lu Y, Huang X et al (2020) Communication-efficient federated learning and permissioned blockchain for digital twin edge networks. IEEE Internet Things J 8:2276–2288

    Article  Google Scholar 

  150. Bouachir O, Aloqaily M et al (2022) FederatedGrids: federated learning and blockchain-assisted P2P energy sharing. IEEE Trans Green Commun Netw

    Google Scholar 

  151. Wang Z, Ogbodo M et al (2020) AEBIS: AI-enabled blockchain-based electric vehicle integration system for power management in smart grid platform. IEEE Access 8:226409–226421

    Article  Google Scholar 

  152. Manimuthu A, Venkatesh VG et al (2021) Design and development of automobile assembly model using federated artificial intelligence with smart contract. Int J Prod Res 20:1–25

    Google Scholar 

  153. Greenspan G (2015) Multichain private blockchain-white paper. https://www.multichain.com/download/MultiChain-White-Paper.pdf

  154. Association Libra (2020) An introduction to libra: White paper. Four Interesting features that make Facebook’s Libra such a big deal, Medium

    Google Scholar 

  155. Saxena A, Goebel K (2008) Turbofan engine degradation simulation data set. NASA Ames Prognostics Data repository, NASA Ames Research Center, Moffett Field

    Google Scholar 

  156. Polap D, Srivastava G et al (2020) Blockchain technology and neural networks for the internet of medical things. In: IEEE INFOCOM 2020-IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp 508–513

    Google Scholar 

  157. Jaeger S, Candemir S et al (2014) Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg 4:475

    Google Scholar 

  158. Shen M, Wang H et al (2020) Exploiting unintended property leakage in blockchain-assisted federated learning for intelligent edge computing. IEEE Internet Things J 8:2265–2275

    Article  Google Scholar 

  159. Ramanan P, Nakayama K (2020) Baffle: Blockchain based aggregator free federated learning. In: 2020 IEEE international conference on blockchain (Blockchain), pp 72–81

    Google Scholar 

  160. Halim SM, Khan L, Thuraisingham B (2020) Next-location prediction using federated learning on a blockchain. In: 2020 IEEE second international conference on cognitive machine intelligence (CogMI), pp 244–250

    Google Scholar 

  161. Aloqaily M, Al Ridhawi I, Guizani M (2021) Energy-Aware Blockchain and Federated Learning-Supported Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, University of Windsor, Windsor, ON, Canada

    Samaneh Miri Rostami, Saeed Samet & Ziad Kobti

Authors
  1. Samaneh Miri Rostami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saeed Samet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziad Kobti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samaneh Miri Rostami .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada

    Roozbeh Razavi-Far

  2. Department of Computer Science, Western University, London, ON, Canada

    Boyu Wang

  3. University of Alberta and the Alberta Machine Intelligence Institute (Amii), Edmonton, AB, Canada

    Matthew E. Taylor

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Kowloon, China

    Qiang Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Miri Rostami, S., Samet, S., Kobti, Z. (2023). A Study of Blockchain-Based Federated Learning. In: Razavi-Far, R., Wang, B., Taylor, M.E., Yang, Q. (eds) Federated and Transfer Learning. Adaptation, Learning, and Optimization, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-031-11748-0_7

Download citation

Publish with us

Policies and ethics