Research Directions for Decentralized Technology Transactions: An Update

Authors

DOI:

https://doi.org/10.56294/saludcyt20252161

Keywords:

Blockchain, decentralized technology, edge devices, federated learning, linkability, privacy, and traceability issues

Abstract

Decentralized technologies such as blockchain and federated learning have emerged as promising solutions to improve privacy, transparency, and security in distributed environments. This paper aims to provide updated research directions concerning the unresolved issues of linkability and traceability in decentralized technology transactions. A systematic review was conducted using Scopus and Web of Science databases, covering studies published between 2017 and 2023. A total of 313 papers were initially identified, screened, and filtered based on inclusion and exclusion criteria, resulting in 29 relevant studies. The analysis indicates that most prior works focused on privacy preservation and incentive mechanisms but neglected linkability and traceability concerns. Several approaches, including ring signatures, CryptoNote protocols, and smart contract-based incentives, were identified as potential solutions. While blockchain–federated learning integration enhances privacy, unresolved traceability and linkability issues still pose significant risks in sensitive domains such as healthcare and finance. Future work should prioritize addressing these issues to ensure secure, anonymous, and scalable decentralized transactions.

References

1. Valerio Stallone, Martin Wetzels, and Michael Klaas. Applications of blockchain technology in marketing—a systematic review of marketing technology companies. Blockchain: Research and Applications, 2(3):100023, 2021.

2. Jiafu Wan, Jiapeng Li, Muhammad Imran, Di Li, et al. A blockchainbased solution for enhancing security and privacy in smart factory. IEEE Transactions on Industrial Informatics, 15(6):3652–3660, 2019.

3. Arpan Bhattacharjee, Shahriar Badsha, Abdur R Shahid, Hanif Livani, and Shamik Sengupta. Block-phasor: A decentralized blockchain framework to enhance security of synchrophasor. In 2020 IEEE Kansas Power and Energy Conference (KPEC), pages 1–6. IEEE, 2020.

4. Sadia Ramzan, Aqsa Aqdus, Vinayakumar Ravi, Deepika Koundal, Rashid Amin, and Mohammed A Al Ghamdi. Healthcare applications using blockchain technology: Motivations and challenges. IEEE Transactions on Engineering Management, 2022.

5. Eugenia Politou, Fran Casino, Efthimios Alepis, and Constantinos Patsakis. Blockchain mutability: Challenges and proposed solutions. IEEE Transactions on Emerging Topics in Computing, 9(4):1972–1986, 2019.

6. Ying Yan, Changzheng Wei, Xuepeng Guo, Xuming Lu, Xiaofu Zheng, Qi Liu, Chenhui Zhou, Xuyang Song, Boran Zhao, Hui Zhang, et al.

7. Confidentiality support over financial grade consortium blockchain. In Proceedings of the 2020 ACM SIGMOD international conference on management of data, pages 2227–2240, 2020.

8. Marianna Belotti, Nikola Božic, Guy Pujolle, and Stefano Secci.´ A vademecum on blockchain technologies: When, which, and how. IEEE Communications Surveys & Tutorials, 21(4):3796–3838, 2019.

9. Adrian Nilsson, Simon Smith, Gregor Ulm, Emil Gustavsson, and Mats Jirstrand. A performance evaluation of federated learning algorithms. In Proceedings of the second workshop on distributed infrastructures for deep learning, pages 1–8, 2018.

10. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273–1282. PMLR, 2017.

11. Max Ryabinin, Eduard Gorbunov, Vsevolod Plokhotnyuk, and Gennady Pekhimenko. Moshpit sgd: Communication-efficient decentralized training on heterogeneous unreliable devices. Advances in Neural Information Processing Systems, 34:18195–18211, 2021.

12. Sean Augenstein, H Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, et al. Generative models for effective ml on private, decentralized datasets. arXiv preprint arXiv:1911.06679, 2019.

13. Jie Xu and Heqiang Wang. Client selection and bandwidth allocation in wireless federated learning networks: A long-term perspective. IEEE Transactions on Wireless Communications, 20(2):1188–1200, 2020.

14. Yunlong Lu, Xiaohong Huang, Yueyue Dai, Sabita Maharjan, and Yan Zhang. Blockchain and federated learning for privacy-preserved data sharing in industrial iot. IEEE Transactions on Industrial Informatics, 16(6):4177–4186, 2019.

15. Youyang Qu, Longxiang Gao, Tom H Luan, Yong Xiang, Shui Yu, Bai Li, and Gavin Zheng. Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet of Things Journal, 2020.

16. Xianglin Bao, Cheng Su, Yan Xiong, Wenchao Huang, and Yifei Hu. Flchain: A blockchain for auditable federated learning with trust and incentive. In 2019 5th International Conference on Big Data Computing and Communications (BIGCOM), pages 151–159. IEEE, 2019.

17. Jiawen Kang, Dongdong Ye, Jiangtian Nie, Jiang Xiao, Xianjun Deng, Siming Wang, Zehui Xiong, Rong Yu, and Dusit Niyato. Blockchainbased federated learning for industrial metaverses: Incentive scheme with optimal aoi. In 2022 IEEE International Conference on Blockchain (Blockchain), pages 71–78. IEEE, 2022.

18. Liang Gao, Li Li, Yingwen Chen, ChengZhong Xu, and Ming Xu. Fgfl: A blockchain-based fair incentive governor for federated learning. Journal of Parallel and Distributed Computing, 163:283–299, 2022.

19. Yanru Chen, Yuanyuan Zhang, Shengwei Wang, Fan Wang, Yang Li, Yuming Jiang, Liangyin Chen, and Bing Guo. 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.

20. Kentaroh Toyoda and Allan N Zhang. Mechanism design for an incentiveaware blockchain-enabled federated learning platform. In 2019 IEEE international conference on big data (Big Data), pages 395–403. IEEE, 2019.

21. Weishan Zhang, Tao Zhou, Qinghua Lu, Xiao Wang, Chunsheng Zhu, Haoyun Sun, Zhipeng Wang, Sin Kit Lo, and Fei-Yue Wang. Dynamicfusion- based federated learning for covid-19 detection. IEEE Internet of Things Journal, 8(21):15884–15891, 2021.

22. Bimal Ghimire and Danda B Rawat. Recent advances on federated learning for cybersecurity and cybersecurity for federated learning for internet of things. IEEE Internet of Things Journal, 9(11):8229–8249, 2022.

23. Muhammad Shayan, Clement Fung, Chris JM Yoon, and Ivan Beschastnikh. Biscotti: A blockchain system for private and secure federated learning. IEEE Transactions on Parallel and Distributed Systems, 32(7):1513– 1525, 2020.

24. Yunlong Lu, Xiaohong Huang, Ke Zhang, Sabita Maharjan, and Yan Zhang. Blockchain and federated learning for 5g beyond. IEEE Network, 35(1):219–225, 2020.

25. Ziyuan Li, Jian Liu, Jialu Hao, Huimei Wang, and Ming Xian. Crowdsfl: A secure crowd computing framework based on blockchain and federated learning. Electronics, 9(5):773, 2020.

26. Sandi Rahmadika and Kyung-Hyune Rhee. Unlinkable collaborative learning transactions: Privacy-awareness in decentralized approaches. IEEE Access, 9:65293–65307, 2021.

27. Sandi Rahmadika, Philip Virgil Astillo, Gaurav Choudhary, Daniel Gerbi Duguma, Vishal Sharma, and Ilsun You. Blockchain-based privacy preservation scheme for misbehavior detection in lightweight iomt devices. IEEE Journal of Biomedical and Health Informatics, 27(2):710–721, 2022.

28. Mansoor Ali, Hadis Karimipour, and Muhammad Tariq. Integration of blockchain and federated learning for internet of things: Recent advances and future challenges. Computers & Security, 108:102355, 2021.

29. Sushil Kumar Singh, Laurence T Yang, and Jong Hyuk Park. Fusionfedblock:: Fusion of blockchain and federated learning to preserve privacy in industry 5.0. 2023.

30. Sandi Rahmadika and Kyung-Hyune Rhee. Enhancing data privacy through a decentralised predictive model with blockchain-based revenue. International Journal of Ad Hoc and Ubiquitous Computing, 37(1):1–15, 2021.

31. Stefano Savazzi, Monica Nicoli, and Vittorio Rampa. Federated learning with cooperating devices: A consensus approach for massive iot networks. IEEE Internet of Things Journal, 7(5):4641–4654, 2020.

32. Sawsan Abdul Rahman, Hanine Tout, Chamseddine Talhi, and Azzam Mourad. Internet of things intrusion detection: Centralized, on-device, or federated learning? IEEE Network, 34(6):310–317, 2020.

33. Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2):1–210, 2021.

34. Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi. Privacypreserving deep inference for rich user data on the cloud. arXiv preprint arXiv:1710.01727, 2017.

35. K Mahalakshmi, K Kousalya, Himanshu Shekhar, Aby K Thomas, L Bhagyalakshmi, Sanjay Kumar Suman, Selvaraj Chandragandhi, Prashant Bachanna, Kannan Srihari, and VenkatesaPrabhu Sundramurthy. Public auditing scheme for integrity verification in distributed cloud storage system. Scientific Programming, 2021:1–5, 2021.

36. Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip Gibbons. The non-iid data quagmire of decentralized machine learning. In International Conference on Machine Learning, pages 4387–4398. PMLR, 2020.

37. Bjarne Pfitzner, Nico Steckhan, and Bert Arnrich. Federated learning in a medical context: a systematic literature review. ACM Transactions on Internet Technology (TOIT), 21(2):1–31, 2021.

38. Chandra Thapa, Pathum Chamikara Mahawaga Arachchige, Seyit Camtepe, and Lichao Sun. Splitfed: When federated learning meets split learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 8485–8493, 2022.

39. István Hegedu˝s, Gábor Danner, and Márk Jelasity. Gossip learning as a decentralized alternative to federated learning. In Distributed Applications and Interoperable Systems: 19th IFIP WG 6.1 International Conference, DAIS 2019, Held as Part of the 14th International Federated Conference on Distributed Computing Techniques, DisCoTec 2019, Kongens Lyngby, Denmark, June 17–21, 2019, Proceedings 19, pages 74–90. Springer, 2019.

40. Tanweer Alam and Ruchi Gupta. Federated learning and its role in the privacy preservation of iot devices. Future Internet, 14(9):246, 2022.

41. Xiaoyuan Liu, Hongwei Li, Guowen Xu, Zongqi Chen, Xiaoming Huang, and Rongxing Lu. Privacy-enhanced federated learning against poisoning adversaries. IEEE Transactions on Information Forensics and Security, 16:4574–4588, 2021.

42. Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, and Yuan Gao. A survey on federated learning. Knowledge-Based Systems, 216:106775, 2021.

43. Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, et al. Papaya: Practical, private, and scalable federated learning. Proceedings of Machine Learning and Systems, 4:814–832, 2022.

44. Jingyan Jiang, Liang Hu, Chenghao Hu, Jiate Liu, and Zhi Wang. Bacombo—bandwidth-aware decentralized federated learning. Electronics, 9(3):440, 2020.

45. Chen Wang, Jian Shen, Jin-Feng Lai, and Jianwei Liu. B-tsca: Blockchain assisted trustworthiness scalable computation for v2i authentication in vanets. IEEE Transactions on Emerging Topics in Computing, 9(3):1386– 1396, 2020.

46. Shafaq Naheed Khan, Faiza Loukil, Chirine Ghedira-Guegan, Elhadj Benkhelifa, and Anoud Bani-Hani. Blockchain smart contracts: Applications, challenges, and future trends. Peer-to-peer Networking and Applications, 14:2901–2925, 2021.

47. Mohammad Saidur Rahman, Ibrahim Khalil, Pathum Chamikara Mahawaga Arachchige, Abdelaziz Bouras, and Xun Yi. A novel architecture for tamper proof electronic health record management system using blockchain wrapper. In Proceedings of the 2019 ACM international symposium on blockchain and secure critical infrastructure, pages 97–105, 2019.

48. Lucianna Kiffer, Dave Levin, and Alan Mislove. Analyzing ethereum’s contract topology. In Proceedings of the Internet Measurement Conference 2018, pages 494–499, 2018.

49. Davide Caputo, Luca Verderame, Andrea Ranieri, Alessio Merlo, and Luca Caviglione. Fine-hearing google home: why silence will not protect your privacy. J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl., 11(1):35–53, 2020.

50. Xuefei Yin, Yanming Zhu, and Jiankun Hu. A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions. ACM Computing Surveys (CSUR), 54(6):1–36, 2021.

51. Faiza Loukil, Khouloud Boukadi, Mourad Abed, and Chirine GhediraGuegan. Decentralized collaborative business process execution using blockchain. World Wide Web, 24(5):1645–1663, 2021.

52. Mohamed Abdur Rahman, M Shamim Hossain, Mohammad Saiful Islam, Nabil A Alrajeh, and Ghulam Muhammad. Secure and provenance enhanced internet of health things framework: A blockchain managed federated learning approach. Ieee Access, 8:205071–205087, 2020.

53. Sandi Rahmadika, Muhammad Firdaus, Yong-Hwan Lee, and KyungHyune Rhee. An investigation of pseudonymization techniques in decentralized transactions. J. Internet Serv. Inf. Secur., 11(4):1–18, 2021.

54. Jiasi Weng, Jian Weng, Jilian Zhang, Ming Li, Yue Zhang, and Weiqi Luo. Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Transactions on Dependable and Secure Computing, 2019.

55. Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov. Machine learning models that remember too much. In Proceedings of the 2017 ACM SIGSAC Conference on computer and communications security, pages 587–601, 2017.

56. Syreen Banabilah, Moayad Aloqaily, Eitaa Alsayed, Nida Malik, and Yaser Jararweh. Federated learning review: Fundamentals, enabling technologies, and future applications. Information processing & management, 59(6):103061, 2022.

57. Dun Li, Dezhi Han, Tien-Hsiung Weng, Zibin Zheng, Hongzhi Li, Han Liu, Arcangelo Castiglione, and Kuan-Ching Li. Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey. Soft Computing, 26(9):4423–4440, 2022.

58. Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pages 3–18. IEEE, 2017.

59. Lan Liu, Yi Wang, Gaoyang Liu, Kai Peng, and Chen Wang. Membership inference attacks against machine learning models via prediction sensitivity. IEEE Transactions on Dependable and Secure Computing, 2022.

60. Jakub Konecnˇ y, H Brendan McMahan, Felix X Yu, Peter Richtárik,` Ananda Theertha Suresh, and Dave Bacon. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492, 2016.

61. Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecnˇ y, Stefano` Mazzocchi, H Brendan McMahan, et al. Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046, 2019.

62. Satoshi Nakamoto and A Bitcoin. A peer-to-peer electronic cash system. Bitcoin.–URL: https://bitcoin. org/bitcoin. pdf, 4(2):15, 2008.

63. Elizabeth Vieira and José Gomes. A comparison of scopus and web of science for a typical university. Scientometrics, 81(2):587–600, 2009.

64. Maxim Chernyshev, Sherali Zeadally, and Zubair Baig. Healthcare data breaches: Implications for digital forensic readiness. Journal of medical systems, 43:1–12, 2019.

65. Alex Biryukov and Sergei Tikhomirov. Security and privacy of mobile wallet users in bitcoin, dash, monero, and zcash. Pervasive and Mobile Computing, 59:101030, 2019.

66. Zhilin Wang, Qin Hu, Ruinian Li, Minghui Xu, and Zehui Xiong. Incentive mechanism design for joint resource allocation in blockchain-based federated learning. IEEE Transactions on Parallel and Distributed Systems, 34(5):1536–1547, 2023.

67. Sandi Rahmadika and Kyung-Hyune Rhee. Merging collaborative learning and blockchain: Privacy in context. In Proceedings of the Korea Information Processing Society Conference, pages 228–230. Korea Information Processing Society, 2020.

68. Chuan Ma, Jun Li, Long Shi, Ming Ding, Taotao Wang, Zhu Han, and H Vincent Poor. When federated learning meets blockchain: A new distributed learning paradigm. IEEE Computational Intelligence Magazine, 17(3):26–33, 2022.

69. Shen Noether. Ring signature confidential transactions for monero. IACR Cryptol. ePrint Arch., 2015:1098, 2015.

70. Aditya Pribadi Kalapaaking, Ibrahim Khalil, and Xun Yi. Blockchainbased federated learning with smpc model verification against poisoning attack for healthcare systems. IEEE Transactions on Emerging Topics in Computing, 12(1):269–280, 2023.

71. Marc Jayson Baucas, Petros Spachos, and Konstantinos N Plataniotis. Federated learning and blockchain-enabled fog-iot platform for wearables in predictive healthcare. IEEE Transactions on Computational Social Systems, 10(4):1732–1741, 2023.

72. Yinbin Miao, Ziteng Liu, Hongwei Li, Kim-Kwang Raymond Choo, and Robert H Deng. Privacy-preserving byzantine-robust federated learning via blockchain systems. IEEE Transactions on Information Forensics and Security, 17:2848–2861, 2022.

73. Saurabh Singh, Shailendra Rathore, Osama Alfarraj, Amr Tolba, and Byungun Yoon. A framework for privacy-preservation of iot healthcare data using federated learning and blockchain technology. Future Generation Computer Systems, 129:380–388, 2022.

74. Zhanpeng Yang, Yuanming Shi, Yong Zhou, Zixin Wang, and Kai Yang. Trustworthy federated learning via blockchain. IEEE Internet of Things Journal, 10(1):92–109, 2022.

75. Umer Majeed, Latif U Khan, Abdullah Yousafzai, Zhu Han, Bang Ju Park, and Choong Seon Hong. St-bfl: A structured transparency empowered cross-silo federated learning on the blockchain framework. Ieee Access, 9:155634–155650, 2021.

76. Hong Liu, Shuaipeng Zhang, Pengfei Zhang, Xinqiang Zhou, Xuebin Shao, Geguang Pu, and Yan Zhang. Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing. IEEE Transactions on Vehicular Technology, 70(6):6073–6084, 2021.

77. Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, and Yingbo Liu. Privacy-preserving blockchain-based federated learning for iot devices. IEEE Internet of Things Journal, 8(3):1817–1829, 2021.

78. Jin Sun, Ying Wu, Shangping Wang, Yixue Fu, and Xiao Chang. Permissioned blockchain frame for secure federated learning. IEEE Communications Letters, 26(1):13–17, 2021.

79. Yuanhang Qi, M Shamim Hossain, Jiangtian Nie, and Xuandi Li. Privacypreserving blockchain-based federated learning for traffic flow prediction. Future Generation Computer Systems, 117:328–337, 2021.

80. Qianlong Wang, Yifan Guo, Xufei Wang, Tianxi Ji, Lixing Yu, and Pan Li. Ai at the edge: Blockchain-empowered secure multiparty learning with heterogeneous models. IEEE Internet of Things Journal, 7(10):9600–9610, 2020.

81. Sandi Rahmadika and Kyung-Hyune Rhee. Reliable collaborative learning with commensurate incentive schemes. In 2020 IEEE International Conference on Blockchain (Blockchain), pages 496–502. IEEE, 2020.

82. Yuan Liu, Zhengpeng Ai, Shuai Sun, Shuangfeng Zhang, Zelei Liu, and Han Yu. Fedcoin: A peer-to-peer payment system for federated learning. In Federated Learning, pages 125–138. Springer, 2020.

83. Sizheng Fan, Hongbo Zhang, Yuchen Zeng, and Wei Cai. Hybrid blockchain-based resource trading system for federated learning in edge computing. IEEE Internet of Things Journal, 2020.

84. Latif U Khan, Shashi Raj Pandey, Nguyen H Tran, Walid Saad, Zhu Han, Minh NH Nguyen, and Choong Seon Hong. Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine, 58(10):88–93, 2020.

85. Meng Shen, Jie Zhang, Liehuang Zhu, Ke Xu, and Xiangyun Tang.

86. Secure svm training over vertically-partitioned datasets using consortium blockchain for vehicular social networks. IEEE Transactions on Vehicular Technology, 69(6):5773–5783, 2020.

87. Hyunil Kim, Seung-Hyun Kim, Jung Yeon Hwang, and Changho Seo. Efficient privacy-preserving machine learning for blockchain network. IEEE Access, 7:136481–136495, 2019.

88. Jiawen Kang, Zehui Xiong, Dusit Niyato, Shengli Xie, and Junshan Zhang. 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.

89. Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. Exploiting unintended feature leakage in collaborative learning. In 2019 IEEE symposium on security and privacy (SP), pages 691– 706. IEEE, 2019.

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Published

2025-09-02

How to Cite

1.
Hadi A, Rahmadika S, Rahmi U, Tatimma Larasati H, Ramadhani Fajri B. Research Directions for Decentralized Technology Transactions: An Update. Salud, Ciencia y Tecnología [Internet]. 2025 Sep. 2 [cited 2025 Sep. 8];5:2161. Available from: https://sct.ageditor.ar/index.php/sct/article/view/2161