Authors : Md Hossain; Nakul Bakchi; Md Imran Hossain; Suman Das; Md Faysal Ahmed

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/49eyuts6

Scribd : https://tinyurl.com/26jm6v8d

DOI : https://doi.org/10.38124/ijisrt/26jun1585

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Factories with similar machines could build better failure-prediction models by sharing data, but most do not want to share their raw records with competitors or a central server. Federated learning (FL) solves this problem by allowing each factory to keep its data locally while training a shared model. However, FL still depends on a central server and does not clearly track contributions. This paper shows a framework that combines FL with a lightweight permissioned ledger. Each factory trains a local model, signs its update, and stores only a hash of the update on the ledger. This provides data integrity, an audit trail, and controlled participation. Using the AI4I 2020 predictive-maintenance dataset across five simulated factories, the framework achieved a ROC-AUC of 0.949, close to the centralized model’s 0.974 and much better than a single factory’s 0.790. The ledger added only about 6% extra training time and required very little storage. This helps identify and remove malicious participants, making industrial data sharing more secure and trustworthy.

Keywords : Federated Learning, Blockchain, Predictive Maintenance, Industry 4.0, Industrial AI, Machine Learning Security.

References :

1. H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Aguera y Arcas, "Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proc. 20th Int. Conf. Artificial Intelligence and Statistics (AISTATS), PMLR vol. 54, 2017, pp. 1273-1282.
2. J. Konecny, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon, "Federated Learning: Strategies for Improving Communication Efficiency," in NIPS Workshop on Private Multi-Party Machine Learning, arXiv:1610.05492, 2016.
3. T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, "Federated Optimization in Heterogeneous Networks," in Proc. Machine Learning and Systems 2 (MLSys), 2020.
4. S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, "SCAFFOLD: Stochastic Controlled Averaging for Federated Learning," in Proc. 37th Int. Conf. Machine Learning (ICML), PMLR vol. 119, 2020, pp. 5132-5143.
5. T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, "Federated Learning: Challenges, Methods, and Future Directions," IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50-60, 2020.
6. P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, et al., "Advances and Open Problems in Federated Learning," Foundations and Trends in Machine Learning, vol. 14, no. 1-2, pp. 1-210, 2021.
7. Q. Li, Z. Wen, Z. Wu, S. Hu, N. Wang, Y. Li, X. Liu, and B. He, "A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection," IEEE Trans. Knowledge and Data Engineering, vol. 35, no. 4, pp. 3347-3366, 2023.
8. Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, "Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT," IEEE Trans. Industrial Informatics, vol. 16, no. 6, pp. 4177-4186, 2020.
9. G. Xu, Z. Zhou, J. Dong, L. Zhang, and X. Song, "A Blockchain-Based Federated Learning Scheme for Data Sharing in Industrial Internet of Things," IEEE Internet of Things Journal, 2023.
10. B. Jia, X. Zhang, J. Liu, Y. Zhang, K. Huang, and Y. Liang, "Blockchain-Enabled Federated Learning Data Protection Aggregation Scheme With Differential Privacy and Homomorphic Encryption in IIoT," IEEE Trans. Industrial Informatics, 2022.
11. A. Yazdinejad, A. Dehghantanha, R. M. Parizi, M. Hammoudeh, H. Karimipour, and G. Srivastava, "Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-Based IIoT Networks," IEEE Trans. Industrial Informatics, 2022.
12. Y. Jiang, B. Ma, X. Wang, G. Yu, P. Yu, Z. Wang, W. Ni, and R. P. Liu, "Blockchained Federated Learning for Internet of Things: A Comprehensive Survey," ACM Computing Surveys, vol. 56, no. 10, art. 258, 2024.
13. D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, D. Niyato, and H. V. Poor, "Federated Learning for Industrial Internet of Things in Future Industries," IEEE Wireless Communications, 2021.
14. B. Farahani and A. K. Monsefi, "Smart and collaborative industrial IoT: A federated learning and data space approach," Digital Communications and Networks, 2023.
15. Y. Li, C. Chen, N. Liu, H. Huang, Z. Zheng, and Q. Yan, "A Blockchain-Based Decentralized Federated Learning Framework with Committee Consensus," IEEE Network, vol. 35, no. 1, pp. 234-241, 2021.
16. H. Kim, J. Park, M. Bennis, and S.-L. Kim, "Blockchained On-Device Federated Learning," IEEE Communications Letters, vol. 24, no. 6, pp. 1279-1283, 2020.
17. D. Li, D. Han, T.-H. Weng, Z. Zheng, H. Li, H. Liu, A. Castiglione, and K.-C. Li, "Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey," Soft Computing, 2021.
18. Y. Qu, M. P. Uddin, C. Gan, Y. Xiang, L. Gao, and J. Yearwood, "Blockchain-enabled Federated Learning: A Survey," ACM Computing Surveys, 2022.
19. S. Ren, E. Kim, and C. Lee, "A scalable blockchain-enabled federated learning architecture for edge computing," PLOS ONE, 2024.
20. W. Ning, Y. Zhu, C. Song, H. Li, L. Zhu, J. Xie, T. Chen, T. Xu, X. Xu, and J. Gao, "Blockchain-Based Federated Learning: A Survey and New Perspectives," Applied Sciences, 2024.
21. B. Du, H. Wang, Y. Li, J. Zhao, Y. Ma, and R. Huang, "Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on Blockchain," ACM Trans. Sensor Networks, 2024.
22. Z. Zhou, Y. Tian, J. Xiong, J. Ma, and C. Peng, "Blockchain-Enabled Secure and Trusted Federated Data Sharing in IIoT," IEEE Trans. Industrial Informatics, 2023.
23. Q. Wang, H. Dong, Y. Huang, Z. Liu, and Y. Gou, "Blockchain-Enabled Federated Learning for Privacy-Preserving Non-IID Data Sharing in Industrial Internet," Computers, Materials and Continua, 2024.
24. J. Sengupta, S. Ruj, and S. Das Bit, "FairShare: Blockchain Enabled Fair, Accountable and Secure Data Sharing for Industrial IoT," IEEE Trans. Network and Service Management, 2023.
25. J. Tan, J. Shi, J. Wan, H.-N. Dai, J. Jin, and R. Zhang, "Blockchain-Based Data Security and Sharing for Resource-Constrained Devices in Manufacturing IoT," IEEE Internet of Things Journal, 2024.
26. W. Tong, L. Yang, Z. Li, X. Jin, and L. Tan, "Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection," Sensors, 2024.
27. W. Wang, H. Huang, Z. Yin, T. R. Gadekallu, M. Alazab, and C. Su, "Smart contract token-based privacy-preserving access control system for industrial Internet of Things," Digital Communications and Networks, 2023.
28. W. Zhang, Z. Wang, and X. Li, "Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis," Reliability Engineering and System Safety, 2023.
29. S. Zhang, P. Jiang, X. Li, C. Yin, and X. V. Wang, "A blockchain-empowered secure federated domain generalization framework for machinery fault diagnosis," Advanced Engineering Informatics, 2024.
30. W. Zhang, Q. Lu, Q. Yu, Z. Li, Y. Liu, S. K. Lo, S. Chen, X. Xu, and L. Zhu, "Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT," IEEE Internet of Things Journal, 2021.
31. Y. Xiao, H. Shao, J. Lin, Z. Huo, and B. Liu, "BCE-FL: A Secure and Privacy-Preserving Federated Learning System for Device Fault Diagnosis Under Non-IID Condition in IIoT," IEEE Internet of Things Journal, 2024.
32. Y. Qin, J. Yang, J. Zhou, H. Pu, X. Zhang, and Y. Mao, "Dynamic weighted federated remaining useful life prediction approach for rotating machinery," Mechanical Systems and Signal Processing, 2023.
33. M. Mehta, S. Chen, H. Tang, and C. Shao, "A federated learning approach to mixed fault diagnosis in rotating machinery," Journal of Manufacturing Systems, 2023.
34. W. Zhang and X. Li, "Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions," Structural Health Monitoring, 2022.
35. J. Ahn, Y. Lee, N. Kim, C. Park, and J. Jeong, "Federated Learning for Predictive Maintenance and Anomaly Detection Using Time Series Data Distribution Shifts in Manufacturing Processes," Sensors, 2023.
36. G. Xia, J. Chen, C. Yu, and J. Ma, "Poisoning Attacks in Federated Learning: A Survey," IEEE Access, 2023.
37. K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, "Practical Secure Aggregation for Privacy-Preserving Machine Learning," in Proc. 2017 ACM SIGSAC Conf. Computer and Communications Security (CCS), 2017.
38. M. Abadi, A. Chu, I. J. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, "Deep Learning with Differential Privacy," in Proc. 2016 ACM SIGSAC Conf. Computer and Communications Security (CCS), 2016, pp. 308-318.
39. J. Chen, J. Xue, Y. Wang, L. Huang, T. Baker, and Z. Zhou, "Privacy-Preserving and Traceable Federated Learning for data sharing in industrial IoT applications," Expert Systems with Applications, 2023.
40. K. M. Sameera, S. Nicolazzo, M. Arazzi, A. Nocera, K. A. Rafidha Rehiman, P. Vinod, and M. Conti, "Privacy-preserving in Blockchain-based Federated Learning systems," Computer Communications, 2024.
41. M. A. Heikkila, "On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps," Transactions on Machine Learning Research (TMLR), 2025.
42. W. Issa, N. Moustafa, B. Turnbull, N. Sohrabi, and Z. Tari, "Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey," ACM Computing Surveys, 2023.
43. M. Shawkat, A. El-desoky, Z. H. Ali, and M. Salem, "Blockchain and federated learning based on aggregation techniques for industrial IoT: A contemporary survey," Peer-to-Peer Networking and Applications, 2025.
44. S. Ali, Q. Li, and A. Yousafzai, "Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey," Ad Hoc Networks, vol. 152, art. 103320, 2024.
45. J. Zhu, J. Cao, D. Saxena, S. Jiang, and H. Ferradi, "Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions," ACM Computing Surveys, vol. 55, no. 11, art. 240, 2023.
46. H. Alaya, A. Ben Letaifa, and A. Rachedi, "State of the art and taxonomy survey on federated learning and blockchain integration in UAV applications," The Journal of Supercomputing, 2025.
47. Y. Zhao, J. Zhao, L. Jiang, R. Tan, D. Niyato, Z. Li, L. Lyu, and Y. Liu, "Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices," IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1817-1829, 2021.
48. F. Yang, Y. Qiao, M. Z. Abedin, and C. Huang, "Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0," IEEE Trans. Industrial Informatics, vol. 18, no. 12, pp. 8755-8764, 2022.
49. X. Tang, Y. Wang, X. Liu, X. Yuan, C. Fan, Y. Hu, and Q. Miao, "Federated graph neural network for privacy-preserved supply chain data sharing," Applied Soft Computing, vol. 168, art. 112475, 2025.
50. H. M. H. A. Alshkeili, S. J. Almheiri, and M. A. Khan, "Privacy-Preserving Interpretability: An Explainable Federated Learning Model for Predictive Maintenance in Sustainable Manufacturing and Industry 4.0," AI, vol. 6, no. 6, art. 117, 2025.
51. S. Matzka, "Explainable Artificial Intelligence for Predictive Maintenance Applications," in Proc. 2020 Third Int. Conf. Artificial Intelligence for Industries (AI4I), 2020, pp. 69-74, doi: 10.1109/AI4I49448.2020.00023.
52. M. Hossain and M. B. Uddin, "Digital twins in additive manufacturing," World Journal of Advanced Engineering Technology and Sciences, vol. 13, no. 2, pp. 909-918, 2024.
53. M. B. Uddin, M. Hossain, and S. Das, "Advancing manufacturing sustainability with industry 4.0 technologies," International Journal of Science and Research Archive, vol. 6, no. 1, pp. 358-366, 2022.
54. M. O. Bouraima, M. H. Suvo, M. J. Islam, M. B. N. Shahin, and D. Nandy, "AI-Driven Data Reliability for Decision-Making in Advanced Manufacturing and Supply Chain Systems," International Journal of Industrial Engineering Research and Development (IJIERD), vol. 14, no. 2, pp. 45-58, 2023. doi: 10.34218/IJIERD_14_02_004.
55. J. Shekh, M. B. N. Shahin, M. J. Islam, M. H. Suvo, and D. Nandy, "ML Framework for Predictive Hazard Monitoring in Smart Manufacturing Environments," International Journal of Scientific Research in Science, Engineering and Technology, vol. 11, no. 6, pp. 583-592, 2024. doi: 10.32628/IJSRSET2512553.
56. Y. A. Bipasha, "Blockchain technology in supply chain management: transparency, security, and efficiency challenges," International Journal of Science and Research Archive, vol. 10, no. 1, pp. 1186-1196, 2023.
57. M. R. Islam, “System Dynamics of Leadership Influence in Sustainable Supply Chains,” World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 03, pp. 509–514, 2025, doi: 10.30574/wjaets.2025.17.3.1584.
58. M. R. Islam, “Digital Leadership and Circular Economy Performance in Sustainable Supply Chains,” World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 03, pp. 503–508, 2025, doi: 10.30574/wjaets.2025.17.3.1583.
59. M. R. Islam, “Circular economy leadership for sustainable industrial transformation: A holistic framework for resilient and resource-efficient growth,” World Journal of Advanced Engineering Technology and Sciences, vol. 17, no. 03, pp. 253–262, 2025, doi: 10.30574/wjaets.2025.17.3.1540.
60. Y. A. Bipasha, M. R. Islam, and M. F. Ahmed, “A blockchain and machine learning framework for secure and transparent digital supply chain management,” International Journal of Innovative Science and Research Technology, vol. 11, no. 3, pp. 945–952, Mar. 2026, doi: 10.38124/IJISRT/26MAR767.
61. M. R. Islam and A. Halim, “Developing a challenge-driven project management framework for sustainable development: An MCDM-based evaluation and prioritization approach,” International Journal of Science and Research Archive, vol. 18, no. 01, pp. 177–187, 2026, doi: 10.30574/ijsra.2026.18.1.0027.
62. M. B. Uddin, M. R. Islam, M. N. Uddin, and A. Halim, “Next-generation plastic recycling: Breakthrough developments and the path toward a circular economy,” World Journal of Advanced Engineering Technology and Sciences, vol. 16, no. 01, pp. 513–527, 2025, doi: 10.30574/wjaets.2025.16.1.1237.
63. R. A. Elbarouni, “A review of AI-driven seismic interpretation, digital twin technology and intelligent reservoir characterization for hydrocarbon exploration,” World Journal of Advanced Engineering Technology and Sciences, vol. 09, no. 01, pp. 513–519, 2023, doi: 10.30574/wjaets.2023.9.1.0147.
64. R. A. Elbarouni, “AI-driven digital twin framework for real-time seismic reservoir monitoring and predictive hydrocarbon production optimization,” World Journal of Advanced Engineering Technology and Sciences, vol. 11, no. 02, pp. 712–720, 2024, doi: 10.30574/wjaets.2024.11.2.0122.
65. R. A. Elbarouni, “Advanced 3D seismic interpretation techniques for accurate subsurface structural mapping and reservoir characterization,” International Journal of Science and Research Archive, vol. 18, no. 03, pp. 992–1002, 2026, doi: 10.30574/ijsra.2026.18.3.0539.
66. R. A. Elbarouni, “Seismic attribute-based fault detection and structural mapping in 3D seismic data for hydrocarbon exploration,” World Journal of Advanced Engineering Technology and Sciences, vol. 18, no. 03, pp. 320–329, 2026, doi: 10.30574/wjaets.2026.18.3.0166.