1. Aich S, Sinai NK, Kumar S, Ali M, Choi YR, Joo M-I, Kim H-C (2022) Protecting personal healthcare record using blockchain & federated learning technologies. In: 2022 24th international conference on advanced communication technology (ICACT), pages 109–112. IEEE

2. Aloqaily M, Al Ridhawi I, Guizani M (2021) Energy-aware blockchain and federated learning-supported vehicular networks. IEEE Trans Intell Transp Syst 23(11):22641–22652

   Google Scholar 

3. Androulaki E, Barger A, Bortnikov V, Cachin C, Christidis K, De Caro A, Enyeart D, Ferris C, Gennady L, Yacov M et al (2018) Hyperledger fabric: a distributed operating system for permissioned blockchains. In: Proceedings of the thirteenth EuroSys conference. pp 1–15

4. Ayaz F, Sheng Z, Tian D, Guan YL (2021) A blockchain based federated learning for message dissemination in vehicular networks. IEEE Trans Veh Technol 71(2):1927–1940

   Google Scholar 

5. Bai J, Zhang Z, Shen B (2022) Internet of vehicles security situation awareness based on intrusion detection protection systems. J Comput Methods Sci Eng 22(1):189–195

   Google Scholar 

6. Berdik D, Otoum S, Schmidt N, Porter D, Jararweh Y (2021) A survey on blockchain for information systems management and security. Inf Process Manag 58(1):102397

   Google Scholar 

7. Bhattacharya P, Tanwar S, Bodkhe U, Tyagi S, Kumar N (2019) Bindaas: blockchain-based deep-learning as-a-service in healthcare 4.0 applications. IEEE Trans Netw Sci Eng 8(2):1242–1255

   Google Scholar 

8. Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Konečnỳ J, Mazzocchi S, McMahan B et al (2019) Towards federated learning at scale: system design. Proc Mach Learn Syst 1:374–388

   Google Scholar 

9. Bouachir O, Aloqaily M, Özkasap Ö, Ali F (2022) Federatedgrids: federated learning and blockchain-assisted p2p energy sharing. IEEE Trans Green Commun Netw 6:424

   Google Scholar 

10. California State Legislature, USA. California consumer privacy act home page.https://www.caprivacy.org/. Online; accessed 14/02/2021

11. Chai H, Leng S, Chen Y, Zhang K (2020) A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles. IEEE Trans Intell Transp Syst 22(7):3975–3986

    Google Scholar 

12. Chakraborty S, Chakraborty S (2022) Proof of federated training: accountable cross-network model training and inference.arXiv: 2204.06919

13. Che T, Liu J, Zhou Y, Ren J, Zhou J, Sheng VS, Dai H, Dou D(2023) Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization. In: Empirical methods in natural language processing (EMNLP). pp 1–18

14. Che T, Zhang Z, Zhou Y, Zhao X, Liu J, Jiang Z, Yan D, Jin R, Dou D (2022) Federated fingerprint learning with heterogeneous architectures. In: IEEE Int Conf on Data Mining (ICDM), pp 31–40. IEEE

15. Chen H, Chen N, Liu H, Zhang H, Xu J, Chen H, Li Y (2021) 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, pp 536–547. Springer

16. Chen J-H, Chen M-R, Zeng G-Q, Weng J-S (2021) Bdfl: a byzantine-fault-tolerance decentralized federated learning method for autonomous vehicle. IEEE Trans Veh Technol 70(9):8639–8652

    Google Scholar 

17. Chen X, Ji J, Luo C, Liao W, Li P (2018) When machine learning meets blockchain: a decentralized, privacy-preserving and secure design. In: 2018 IEEE international conference on big data (big data), pages 1178–1187. IEEE

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

19. Cheng X, Tian W, Shi F, Zhao M, Chen S, Wang H (2022) A blockchain-empowered cluster-based federated learning model for blade icing estimation on IoT-enabled wind turbine. IEEE Trans Ind Inf 18:9184

    Google Scholar 

20. Cook S (2012) CUDA programming: a developer’s guide to parallel computing with GPUs. Newnes

21. Crain T, Natoli C, Gramoli V (2021) Red belly: a secure, fair and scalable open blockchain. In: 2021 IEEE Symposium on Security and Privacy (SP), pp 466–483. IEEE

22. Deng Y, Han T, Zhang N (2021) Flex: trading edge computing resources for federated learning via blockchain. In: IEEE INFOCOM 2021-IEEE conference on computer communications workshops (INFOCOM WKSHPS), pages 1–2. IEEE

23. Esposito C, Ficco M, Gupta BB (2021) Blockchain-based authentication and authorization for smart city applications. Inf Process Manag 58(2):102468

    Google Scholar 

24. Fan S, Zhang H, Wang Z, Cai W (2022) Mobile devices strategies in blockchain-based federated learning: a dynamic game perspective. IEEE Trans Netw Sci Eng 10(3):1376–1388

    MathSciNet  Google Scholar 

25. Feng L, Yang Z, Guo S, Qiu X, Li W, Yu P (2021) Two-layered blockchain architecture for federated learning over the mobile edge network. IEEE Network 36(1):45–51

    Google Scholar 

26. Gaff BM, Sussman HE, Geetter J (2014) Privacy and big data. Computer 47(6):7–9

    Google Scholar 

27. Gai K, Guo J, Zhu L, Shui Yu (2020) Blockchain meets cloud computing: a survey. IEEE Commun Surv Tutor 22(3):2009–2030

    Google Scholar 

28. Garay J, Kiayias A (2020) Sok: a consensus taxonomy in the blockchain era. In: Cryptographers’ track at the RSA conference, pp 284–318. Springer

29. Han J, Ma Y, Han Y, Zhang Y, Huang G (2022) Demystifying swarm learning: a new paradigm of blockchain-based decentralized federated learning.arXiv:2201.05286

30. He Y, Huang K, Zhang G, Yu FR, Chen J, Li J (2021) Bift: a blockchain-based federated learning system for connected and autonomous vehicles. IEEE Internet Things J 9:12311

    Google Scholar 

31. Hu Q, Wang Z, Xu M, Cheng X (2021) Blockchain and federated edge learning for privacy-preserving mobile crowdsensing. IEEE Internet Things J 10(14):12000

    Google Scholar 

32. Hu S, Li J, Zhang C, Zhao Q, Ye W (2021) The blockchain-based edge computing framework for privacy-preserving federated learning. In: IEEE int conf on blockchain (Blockchain), pp 566–571

33. Huang X, Yuhang W, Liang C, Chen Q, Zhang J (2023) Distance-aware hierarchical federated learning in blockchain-enabled edge computing network. IEEE Internet Things J 10(21):19163–19176

    Google Scholar 

34. Issa W, Moustafa N, Turnbull B, Sohrabi N, Tari Z (2023) Blockchain-based federated learning for securing internet of things: a comprehensive survey. ACM Comput Surv 55(9):1–43

    Google Scholar 

35. Juncheng J, Ji L, Chendi Z, Hao T, Mianxiong D, Dejing D (2023) Efficient asynchronous federated learning with sparsification and quantization. Concurr Comput Pract Exp.https://doi.org/10.1002/cpe.8002

    Article  Google Scholar 

36. Jiang S, Jie W (2022) A reward response game in the blockchain-powered federated learning system. Int J Parallel Emergent Distrib Syst 37(1):68–90

    Google Scholar 

37. Jin J, Ren J, Zhou Y, Lyu L, Liu J, Dou D (2022) Accelerated federated learning with decoupled adaptive optimization. In: Int conf on machine learning (ICML), pp 10298–10322. PMLR

38. Jouppi NP, Young C, Patil N, Patterson D, Agrawal G, Bajwa R, Bates S, Bhatia S, Boden N, Borchers Al et al (2017) In-datacenter performance analysis of a tensor processing unit. In: Int symposium on computer architecture (ISCA), pp 1–12

39. Kalodner H, Möser M, Lee K, Goldfeder S, Plattner M, Chator A, Narayanan A (2020)\(\{\)BlockSci\(\}\): design and applications of a blockchain analysis platform. In: 29th USENIX security symposium (USENIX Security 20), pages 2721–2738

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

    Google Scholar 

41. Jiawen K, Zehui X, Dusit N, Dongdong Y, Dong In K, Jun Z (2019) Toward secure blockchain-enabled internet of vehicles: optimizing consensus management using reputation and contract theory. IEEE Trans Veh Technol 68(3):2906–2920

    Google Scholar 

42. Kang J, Xiong Z, Niyato D, Zou Y, Zhang Y, Guizani M (2020) Reliable federated learning for mobile networks. IEEE Wirel Commun 27(2):72–80

    Google Scholar 

43. Karimireddy SP, Kale S, Mohri M, Reddi S, Stich S, Suresh AT (2020) Scaffold: stochastic controlled averaging for federated learning. In: International conference on machine learning. pp 5132–5143. PMLR

44. Khan LU, Saad W, Han Z, Hong CS (2021) Dispersed federated learning: vision, taxonomy, and future directions. IEEE Wirel Commun 28(5):192–198

    Google Scholar 

45. Kim D, Doh I, Chae K (2021) Improved raft algorithm exploiting federated learning for private blockchain performance enhancement. In: 2021 international conference on information networking (ICOIN), pp 828–832. IEEE

46. Kolb J, AbdelBaky M, Katz RH, Culler DE (2020) Core concepts, challenges, and future directions in blockchain: a centralized tutorial. ACM Comput Surv 53(1):1–39

    Google Scholar 

47. Kong Q, Yin F, Xiao Y, Li B, Yang X, Cui S (2021) Achieving blockchain-based privacy-preserving location proofs under federated learning. In: ICC 2021-IEEE international conference on communications. pp 1–6. IEEE

48. Kumar R, Khan AA, Kumar J, Golilarz NA, Zhang S, Ting Y, Zheng C, Wang W et al (2021) Blockchain-federated-learning and deep learning models for covid-19 detection using CT imaging. IEEE Sens J 21(14):16301–16314

    Google Scholar 

49. Li D, Han D, Weng T-H, Zheng Z, Li H, Liu H, Castiglione A, Li K-C (2022) Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey. Soft Comput 26(9):4423–4440

    Google Scholar 

50. Li G, Hu Y, Zhang M, Liu J, Yin Q, Peng Y, Dou D (2022) Fedhisyn: a hierarchical synchronous federated learning framework for resource and data heterogeneity. In: Int conf on parallel processing (ICPP). pp 1–11

51. Li J, Shao Y, Wei K, Ding M, Ma C, Shi L, Han Z, Poor V (2021) Blockchain assisted decentralized federated learning (blade-fl): performance analysis and resource allocation. IEEE Trans Parallel Distrib Syst 33:2401

    Google Scholar 

52. Li Q, He B, Song D (2021) Model-contrastive federated learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10713–10722

53. Li Q, Wen Z, Wu Z, Hu S, Wang N, Li Y, Liu X, He B (2021) A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Trans Knowl Data Eng 35:3347

    Google Scholar 

54. Li T, Hu S, Beirami A, Smith V (2021) Ditto: fair and robust federated learning through personalization. In: International conference on machine learning. pp 6357–6368. PMLR

55. Tian L, Anit Kumar S, Manzil Z, Maziar S, Ameet T, Virginia S (2020) Federated optimization in heterogeneous networks. Proc Mach Learn Syst 2:429–450

    Google Scholar 

56. Li X, Jiang M, Zhang X, Kamp M, Dou Q (2021) Fedbn: federated learning on non-iid features via local batch normalization.arXiv:2102.07623

57. Li Y, Chen C, Liu N, Huang H, Zheng Z, Yan Q (2020) A blockchain-based decentralized federated learning framework with committee consensus. IEEE Network 35(1):234–241

    Google Scholar 

58. Li Z, Zhou Y, Wu D, Wang R (2021) Local model update for blockchain enabled federated learning: approach and analysis. In: IEEE int conf on blockchain (Blockchain). pp 113–121

59. Liang W, Fan Y, Li K-C, Zhang D, Gaudiot J-L (2020) Secure data storage and recovery in industrial blockchain network environments. IEEE Trans Ind Inf 16(10):6543–6552

    Google Scholar 

60. Liu H, Zhang S, Zhang P, Zhou X, Shao X, Geguang P, Zhang Y (2021) Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing. IEEE Trans Veh Technol 70(6):6073–6084

    Google Scholar 

61. Liu J, Che T, Zhou Y, Jin R, Dai H, Dou D, Valduriez P (2023) Aedfl: efficient asynchronous decentralized federated learning with heterogeneous devices. In: SIAM conference on data mining, pp 1–15

62. Liu J, Huang J, Zhou Y, Li X, Ji S, Xiong H, Dou D (2022) From distributed machine learning to federated learning: a survey. Knowl Inf Syst 64(4):885–917

    Google Scholar 

63. Liu J, Jia J, Che T, Huo C, Ren J, Zhou Y, Dai H, Dou D(2023) Fedasmu: efficient asynchronous federated learning with dynamic staleness-aware model update. In: AAAI. pp 1–18

64. Liu J, Jia J, Ma B, Zhou C, Zhou J, Zhou Y, Dai H, Dou D (2022) Multi-job intelligent scheduling with cross-device federated learning. IEEE Trans Parallel Distrib Syst 34(2):535–551

    Google Scholar 

65. Liu J, Wu Z, Feng D, Zhang M, Wu X, Yao X, Yu D, Ma Y, Zhao F, Dou D (2023) Heterps: distributed deep learning with reinforcement learning based scheduling in heterogeneous environments. Future Gener Comput Syst 148:106

    Google Scholar 

66. Ji L, Xuehai Z, Lei M, Shilei J, Yuan L, Li Zheng G, Qin DD (2023) Distributed and deep vertical federated learning with big data. Concur Comput Pract Exp 35:e7697

    Google Scholar 

67. Liu Y, Ai Z, Sun S, Zhang S, Liu Z, Yu H (2020) Fedcoin: a peer-to-peer payment system for federated learning. In: Federated Learning, pp 125–138. Springer

68. Lo S K, Liu Y, Lu Q, Wang X, Xu X, Paik H-Y, Zhu L (2021) Blockchain-based trustworthy federated learning architecture.arXiv:2108.06912

69. Lu Y, Huang X, Dai Y, Maharjan S, Zhang Y (2019) Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Trans Ind Inf 16(6):4177–4186

    Google Scholar 

70. Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2020) Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans Veh Technol 69(4):4298–4311

    Google Scholar 

71. Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2020) Communication-efficient federated learning and permissioned blockchain for digital twin edge networks. IEEE Internet Things J 8(4):2276–2288

    Google Scholar 

72. Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2020) Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks. IEEE Trans In Inf 17(7):5098–5107

    Google Scholar 

73. Lugan S, Desbordes P, Brion E, Tormo LXR, Legay A, Macq B (2019) Secure architectures implementing trusted coalitions for blockchained distributed learning (tclearn). IEEE Access 7:181789–181799

    Google Scholar 

74. Lyu L, Xu X, Wang Q, Yu H (2020) Collaborative fairness in federated learning. In: Federated Learning. pp 189–204. Springer

75. Ma C, Li J, Ding M, Shi L, Wang T, Han Z, Poor HV (2020) When federated learning meets blockchain: a new distributed learning paradigm.arXiv:2009.09338

76. Ma S, Cao Y, Xiong L (2021) Transparent contribution evaluation for secure federated learning on blockchain. In: 2021 IEEE 37th international conference on data engineering workshops (ICDEW). pp 88–91. IEEE

77. McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics. pp 1273–1282. PMLR

78. Mittal N, Pal S, Joshi A, Sharma A, Tayal S, Sharma Y (2021) Comparative analysis of various platforms of blockchain. Smart and Sustainable Intelligent Systems,. pp 323–340

79. Mohammed MA, Lakhan A, Abdulkareem KH, Zebari DA, Nedoma J, Martinek R, Kadry S, Garcia-Zapirain B (2023) Energy-efficient distributed federated learning offloading and scheduling healthcare system in blockchain based networks. Internet Things 22:100815

    Google Scholar 

80. Mothukuri V, Parizi RM, Pouriyeh S, Dehghantanha A, Choo KKR (2021) FabricFL: blockchain-in-the-loop federated learning for trusted decentralized systems. IEEE Syst J 16(3):3711–3722

    Google Scholar 

81. Moudoud H, Cherkaoui S, Khoukhi L (2021) Towards a secure and reliable federated learning using blockchain. In: IEEE global communications conf. (GLOBECOM). pp 1–6 (2021)

82. Myrzashova R, Alsamhi SH, Shvetsov AV, Hawbani A, Wei X (2023) Blockchain meets federated learning in healthcare: a systematic review with challenges and opportunities. IEEE Internet Things J

83. Nguyen Dinh C, Ming D, Quoc-Viet P, Pathirana Pubudu N, Bao LL, Aruna S, Jun L, Dusit N, Vincent PH (2021) Federated learning meets blockchain in edge computing: opportunities and challenges. IEEE Internet Things J 8:12806

    Google Scholar 

84. Nguyen DC, Hosseinalipour S, Love DJ, Pathirana PN, Brinton CG (2022) Latency optimization for blockchain-empowered federated learning in multi-server edge computing.arXiv:2203.09670

85. Official Journal of the European Union. General data protection regulation.https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679, (2016). Online; accessed 12/02/2021

86. Safa O, Ismaeel AR, Hussein M (2022) A federated learning and blockchain-enabled sustainable energy-trade at the edge: a framework for industry 4.0. IEEE Internet Things J 10:3018

    Google Scholar 

87. Otoum S, Al Ridhawi I, Mouftah HT (2020) Blockchain-supported federated learning for trustworthy vehicular networks. In: GLOBECOM 2020-2020 IEEE global communications conference, pp 1–6. IEEE

88. Ouyang L, Wang F-Y, Tian Y, Jia X, Qi H, Wang G (2023) Artificial identification: a novel privacy framework for federated learning based on blockchain. IEEE Trans Comput Soc Syst 10(6):3576–3585

    Google Scholar 

89. Pandey SR, Tran NH, Bennis M, Tun YK, Manzoor A, Hong CS (2020) A crowdsourcing framework for on-device federated learning. IEEE Trans Wirel Commun 19(5):3241–3256

    Google Scholar 

90. Passerat-Palmbach J, Farnan T, McCoy M, Harris JD, Manion ST, Flannery HL, Gleim B (2020) Blockchain-orchestrated machine learning for privacy preserving federated learning in electronic health data. In: IEEE int conf on blockchain (Blockchain), pp 550–555

91. Passerat-Palmbach J, Farnan T, Miller R, Gross MS, Flannery HL, Gleim B (2019) A blockchain-orchestrated federated learning architecture for healthcare consortia.arXiv:1910.12603

92. Peng Y, Du M, Li F, Cheng R, Song D (2020) Falcondb: blockchain-based collaborative database. In: Proceedings of the 2020 ACM SIGMOD international conference on management of data, pp 637–652

93. Pokhrel SR (2021) Blockchain brings trust to collaborative drones and leo satellites: an intelligent decentralized learning in the space. IEEE Sens J 21(22):25331–25339

    Google Scholar 

94. Pokhrel SR, Choi J (2020) A decentralized federated learning approach for connected autonomous vehicles. In: 2020 IEEE wireless communications and networking conference workshops (WCNCW), pages 1–6. IEEE

95. Qammar A, Karim A, Ning H, Ding J (2023) Securing federated learning with blockchain: a systematic literature review. Artif Intell Rev 56(5):3951–3985

    Google Scholar 

96. Xidi Q, Wang S, Qin H, Cheng X (2021) Proof of federated learning: a novel energy-recycling consensus algorithm. IEEE Trans Parallel Distrib Syst 32(8):2074–2085

    Google Scholar 

97. Youyang Q, Gao L, Luan TH, Xiang Y, Shui Yu, Li B, Zheng G (2020) Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet Things J 7(6):5171–5183

    Google Scholar 

98. Rahmadika S, Firdaus M, Jang S, Rhee K-H (2021) Blockchain-enabled 5g edge networks and beyond: an intelligent cross-silo federated learning approach. Secur Commun Netw 2021:1–14

    Google Scholar 

99. Rahman MA, Hossain MS, Islam MS, Alrajeh NA, Muhammad G (2020) Secure and provenance enhanced internet of health things framework: a blockchain managed federated learning approach. IEEE Access 8:205071–205087

    Google Scholar 

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

101. Shan Z, Ren K, Blanton M, Wang C (2018) Practical secure computation outsourcing: a survey. ACM Comput Surv 51(2):1–40

     Google Scholar 

102. Shayan M, Fung C, Yoon CJM, Beschastnikh I (2020) Biscotti: a blockchain system for private and secure federated learning. IEEE Trans Parallel Distrib Syst 32(7):1513–1525

     Google Scholar 

103. Shi S, He D, Li L, Kumar N, Khan MK, Choo K-KR (2020) Applications of blockchain in ensuring the security and privacy of electronic health record systems: a survey. Comput Secur 97:101966

     Google Scholar 

104. Singh SK, Yang LT, Park JH (2023) Fusionfedblock: fusion of blockchain and federated learning to preserve privacy in industry 5.0. Inf Fusion 90:233–240

     Google Scholar 

105. Standing Committee of the National People’s Congress. Cybersecurity law of the people’s republic of china.https://www.newamerica.org/cybersecurity-initiative/digichina/blog/translation-cybersecurity-law-peoples-republic-china/. Online. Accessed 22/02/2021

106. Sun J, Ying W, Wang S, Yixue F, Chang X (2021) Permissioned blockchain frame for secure federated learning. IEEE Commun Lett 26(1):13–17

     Google Scholar 

107. Tian Y, Li T, Jinbo Xiong Md, Bhuiyan ZA, Ma J, Peng C (2021) A blockchain-based machine learning framework for edge services in IIoT. IEEE Trans Ind Inf 18(3):1918–1929

     Google Scholar 

108. Toyoda K, Zhao J, Neng Sheng ZA, Mathiopoulos PT (2020) Blockchain-enabled federated learning with mechanism design. IEEE Access 8:219744–219756

     Google Scholar 

109. Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R, Zhou Y (2019) A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM workshop on artificial intelligence and security. pp 1–11

110. Xuezhen T, Kun Z, Cong LN, Dusit N, Yang Z, Juan L (2022) Incentive mechanisms for federated learning: from economic and game theoretic perspective. IEEE Trans Cogn Commun Netw 8:1566

     Google Scholar 

111. ur Habib RM, Mukhtar DA, Khaled S, Ernesto D, Davor S (2021) Trustfed: a framework for fair and trustworthy cross-device federated learning in IIoT. IEEE Trans Ind Inf 17(12):8485–8494

     Google Scholar 

112. Wan Y, Youyang Q, Gao L, Xiang Y (2022) Privacy-preserving blockchain-enabled federated learning for b5g-driven edge computing. Comput Netw 204:108671

     Google Scholar 

113. Wang R, Tsai W-T (2022) Asynchronous federated learning system based on permissioned blockchains. Sensors 22(4):1672

     Google Scholar 

114. Wang W, Hoang DT, Hu P, Xiong Z, Niyato D, Wang P, Wen Y, Kim DI (2019) A survey on consensus mechanisms and mining strategy management in blockchain networks. IEEE Access 7:22328–22370

     Google Scholar 

115. Wang Z, Yan B, Yao Y (2021) Blockchain empowered federated learning for medical data sharing model. In: International conference on wireless algorithms, systems, and applications. pp 537–544. Springer

116. Wang Z, Hu Q (2021) Blockchain-based federated learning: a comprehensive survey.arXiv:2110.02182

117. Wang Z, Hu Q, Li R, Xu M, Xiong Z (2022) Incentive mechanism design for joint resource allocation in blockchain-based federated learning.arXiv:2202.10938

118. Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R et al (2021) Swarm learning for decentralized and confidential clinical machine learning. Nature 594(7862):265–270

     Google Scholar 

119. Weng J, Weng J, Zhang J, Li M, Zhang Y, Luo W (2019) Deepchain: auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Trans Dependable Secure Comput 18(5):2438–2455

     Google Scholar 

120. Xiong Z, Zhang Y, Niyato D, Wang P, Han Z (2018) When mobile blockchain meets edge computing. IEEE Commun Mag 56(8):33–39

     Google Scholar 

121. Xu M, Zou Z, Cheng Y, Hu Q, Yu D, Cheng X (2022) Spdl: blockchain-secured and privacy-preserving decentralized learning.arXiv:2201.01989

122. Yang F, Abedin MZ, Hajek P (2023) An explainable federated learning and blockchain-based secure credit modeling method. Eur J Oper Res

123. Yang F, Qiao Y, Abedin MZ, Huang C (2022) Privacy-preserved credit data sharing integrating blockchain and federated learning for industrial 4.0. IEEE Trans Ind Inf 18(12):8755–8764

     Google Scholar 

124. Ye M, Fang X, Du B, Yuen PC, Tao D (2023) Heterogeneous federated learning: state-of-the-art and research challenges. ACM Comput Surv 56(3):1–44

     Google Scholar 

125. Yin X, Zhu Y, Jiankun H (2021) A comprehensive survey of privacy-preserving federated learning: a taxonomy, review, and future directions. ACM Comput Surv 54(6):1–36

     Google Scholar 

126. Yu H, Nikolić I, Hou R, Saxena P (2020) Ohie: blockchain scaling made simple. In: 2020 IEEE symposium on security and privacy (SP). pp 90–105. IEEE

127. Yurochkin M, Agarwal M, Ghosh S, Greenewald K, Hoang N, Khazaeni Y (2019) Bayesian nonparametric federated learning of neural networks. In: International conference on machine learning. pp 7252–7261. PMLR

128. Zeng R, Zeng C, Wang X, Li B, Chu X (2021) A comprehensive survey of incentive mechanism for federated learning.arXiv:2106.15406

129. Zhan Y, Li P, Zhihao Q, Zeng D, Guo S (2020) A learning-based incentive mechanism for federated learning. IEEE Internet Things J 7(7):6360–6368

     Google Scholar 

130. Zhang F, Guo S, Qiu X, Xu S, Qi F, Wang Z (2021) Federated learning meets blockchain: state channel-based distributed data-sharing trust supervision mechanism. IEEE Internet Things J 10(14):12066–12076

     Google Scholar 

131. Zhang H, Liu J, Jia J, Zhou Y, Dai H, Dou D (2022) Fedduap: federated learning with dynamic update and adaptive pruning using shared data on the server. In: Int joint conf on artificial intelligence (IJCAI)

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

133. 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), pages 1–6. IEEE

134. Zhang X, Li F, Zhang Z, Li Q, Wang C, Wu J (2020) Enabling execution assurance of federated learning at untrusted participants. In: IEEE INFOCOM 2020-IEEE conference on computer communications, pp 1877–1886. IEEE

135. Zhang X, Hong M, Dhople S, Yin W, Liu Y (2020) Fedpd: a federated learning framework with optimal rates and adaptivity to non-iid data.arXiv: Learning

136. Zhang Z, Dong D, Ma Y, Ying Y, Jiang D, Chen K, Shou L, Chen G (2021) Refiner: a reliable incentive-driven federated learning system powered by blockchain. Proc VLDB Endowment 14(12):2659–2662

     Google Scholar 

137. Zhao J, Wu X, Zhang Y, Wu Y, Wang Z (2021) A blockchain based decentralized gradient aggregation design for federated learning. In: International conference on artificial neural networks, pp 359–371. Springer

138. Zhao Y, Zhao J, Jiang L, Tan R, Niyato D (2020) Mobile edge computing, blockchain and reputation-based crowdsourcing IoT federated learning: a secure, decentralized and privacy-preserving system.arXiv:1906.10893

139. Zheng Z, Zhou Y, Sun Y, Wang Z, Liu B, Li K (2022) Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges. Connect Sci 34(1):1–28

     Google Scholar 

140. Zhou C, Liu J, Jia J, Zhou J, Zhou Y, Dai H, Dou D (2022) Efficient device scheduling with multi-job federated learning. AAAI Conf Artif Intell 36:9971–9979

     Google Scholar 

141. Zhu J, Cao J, Saxena D, Jiang S, Ferradi H (2023) Blockchain-empowered federated learning: challenges, solutions, and future directions. ACM Comput Surv 55(11):1–31

     Google Scholar 

Download references