ARCHIVES
Original Article
Blockchain-Enabled Secure Federated Learning for Neuroimaging in Distributed Neurology
Gnanesh Methari1
Sugandh Raj Madhira2
Mounika Nuthula3
1 Department of Information Technology (cybersecurity), Franklin University, USA. 2 Department of Business Analytics, Sacred Heart University, USA. 3 Department of Information systems, Trine University, USA.
Published Online: March-April 2026
Pages: 42-50
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260602007
References
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8. M. S and J. K R, “Blockchain-IoMT-enabled federated learning: An intelligent privacy-preserving control policy for electronic health
records,” Array, vol. 28, p. 100586, Dec. 2025, doi: https://doi.org/10.1016/j.array.2025.100586.
9. C. Yen, C.-L. Lin, and M.-C. Chiang, “Exploring the frontiers of neuroimaging: A review of recent advances in understanding brain
functioning and disorders,” Life, vol. 13, no. 7, pp. 1–27, Jul. 2023, doi: https://doi.org/10.3390/life13071472.
10. L. A. Whitten, Functional Magnetic Resonance Imaging (fMRI): An Invaluable Tool in Translational Neuroscience. Research Triangle
Park (NC): RTI Press, 2012. Available: https://www.ncbi.nlm.nih.gov/books/NBK538909/
11. Y. Sun, W. Chen, and Z. Sun, “Multi-modal learning methods in medical imaging area: A survey,” Digital Signal Processing, vol. 167,
p. 105441, Dec. 2025, doi: https://doi.org/10.1016/j.dsp.2025.105441.
12. K. V. Sarma et al., “Federated learning improves site performance in multicenter deep learning without data sharing,” Journal of the
American Medical Informatics Association: JAMIA, vol. 28, no. 6, pp. 1259–1264, Jun. 2021, doi:
https://doi.org/10.1093/jamia/ocaa341.
13. S. R. Abbas, Z. Abbas, A. Zahir, and S. W. Lee, “Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy,
Security, and Predictive Analytics with IoT Integration,” Healthcare, vol. 12, no. 24, pp. 2587–2587, Dec. 2024, doi:
https://doi.org/10.3390/healthcare12242587.
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954–971, Nov. 2017, doi: https://doi.org/10.1037/neu0000426.
15. F. M. AbdelSalam, “Blockchain Revolutionizing Healthcare Industry: A Systematic Review of Blockchain Technology Benefits and
Threats,” Perspectives in Health Information Management, vol. 20, no. 3, p. 1b, Sep. 2023, Available:
https://pmc.ncbi.nlm.nih.gov/articles/PMC10701638/
16. J. Andrew, D. P. Isravel, K. M. Sagayam, B. Bhushan, Y. Sei, and J. Eunice, “Blockchain for Healthcare systems: Architecture, Security
challenges, Trends and Future Directions,” Journal of Network and Computer Applications, vol. 215, pp. 103633–103633, Jun. 2023,
doi: https://doi.org/10.1016/j.jnca.2023.103633.
17. Y. Chang, C. Fang, and W. Sun, “A Blockchain-Based Federated Learning Method for Smart Healthcare,” Computational Intelligence
and Neuroscience, vol. 2021, pp. 1–12, Nov. 2021, doi: https://doi.org/10.1155/2021/4376418.
18. S. Paul, A. Mitra, S. Ghosh, and A. Podder, “Federated Learning for Decentralized Healthcare: Privacy, Efficiency, and Scalability in
Healthcare 5.0,” Studies in Computational Intelligence, pp. 427–444, 2026, doi: https://doi.org/10.1007/978-3-032-03985-9_19.
19. G. Ciaburro, “Benefits and use of blockchain technology to support supply chain during COVID-19,” Lessons from COVID-19, pp.
171–211, 2022, doi: https://doi.org/10.1016/b978-0-323-99878-9.00003-0.
20. S. S. Sefati, R. Craciunescu, B. Arasteh, S. Halunga, O. Fratu, and I. Tal, “Cybersecurity in a Scalable Smart City Framework Using
Blockchain and Federated Learning for Internet of Things (IoT),” Smart Cities, vol. 7, no. 5, pp. 2802–2841, Oct. 2024, doi:
https://doi.org/10.3390/smartcities7050109.
21. W. Ning et al., “Blockchain-Based Federated Learning: A Survey and New Perspectives,” Applied Sciences, vol. 14, no. 20, pp. 9459–
9459, Oct. 2024, doi: https://doi.org/10.3390/app14209459.
22. M. M. Ibrahimy, A. Norta, and P. Normak, “Blockchain-Based Governance Models Supporting Corruption-Transparency: A Systematic
Literature Review,” Blockchain: Research and Applications, vol. 5, no. 2, p. 100186, Dec. 2023, doi:
https://doi.org/10.1016/j.bcra.2023.100186.
23. J. Huang, K. Zhou, A. Xiong, and D. Li, “Smart Contract Vulnerability Detection Model Based on Multi-Task Learning,” Sensors, vol.
22, no. 5, p. 1829, Feb. 2022, doi: https://doi.org/10.3390/s22051829.
24. N. M. Singh et al., “How Machine Learning is Powering Neuroimaging to Improve Brain Health,” Neuroinformatics, Mar. 2022, doi:
https://doi.org/10.1007/s12021-022-09572-9.
25. T. Henry, L. Assekour, A. Rapetti, A. Del Pozzo, and S. Tucci-Piergiovanni, “Secrets on the Chain: Cryptographic Blockchain Patterns
for Verifiable and Confidential Data Handling,” Blockchain: Research and Applications, p. 100422, Dec. 2025, doi:
https://doi.org/10.1016/j.bcra.2025.100422.
26. Y. Tang et al., “A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis,” Cmes-computer
Modeling in Engineering & Sciences, vol. 139, no. 3, pp. 2451–2477, Jan. 2024, doi: https://doi.org/10.32604/cmes.2024.030084.
27. Z. L. Teo et al., “Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture,”
Cell Reports Medicine, vol. 5, no. 2, p. 101419, Feb. 2024, doi: https://doi.org/10.1016/j.xcrm.2024.101419.
28. M. A. Sahid, M. P. Uddin, H. Saha, and M. R. Islam, “Towards privacy-preserving Alzheimer’s disease classification: Federated
learning on T1-weighted magnetic resonance imaging data,” DIGITAL HEALTH, vol. 10, Jan. 2024, doi:
https://doi.org/10.1177/20552076241295577.
29. A. Kouli, K. M. Torsney, and W.-L. Kuan, “Parkinson’s Disease: Etiology, Neuropathology, and Pathogenesis,” Parkinson’s Disease:
Pathogenesis and Clinical Aspects, vol. 1, no. 1, pp. 3–26, 2018, doi:
https://doi.org/10.15586/codonpublications.parkinsonsdisease.2018.ch1.
30. Mosleh Hmoud Al-Adhaileh, S. Ahmad, A. A. Alharbi, M. Alarfaj, Mukta Dhopeshwarkar, and Theyazn H H Aldhyani, “Diagnosis of
epileptic seizure neurological condition using EEG signal: a multi-model algorithm,” Frontiers in Medicine, vol. 12, May 2025, doi:
https://doi.org/10.3389/fmed.2025.1577474.
31. K. Ghamdi and J. AlMusailhi, “Attention-deficit Hyperactivity Disorder and Autism Spectrum Disorder: Towards Better Diagnosis andManagement,” Medicinski arhiv, vol. 78, no. 2, pp. 159–159, Jan. 2024, doi: https://doi.org/10.5455/medarh.2024.78.159-163.
32. P. Ratta, A. Kaur, S. Sharma, M. Shabaz, and G. Dhiman, “Application of Blockchain and Internet of Things in Healthcare and Medical
Sector: Applications, Challenges, and Future Perspectives,” Journal of Food Quality, vol. 2021, pp. 1–20, May 2021, doi:
https://doi.org/10.1155/2021/7608296.
33. Z. Guo, “Blockchain-enhanced smart contracts for formal verification of IoT access control mechanisms,” Alexandria Engineering
Journal, vol. 118, pp. 315–324, Apr. 2025, doi: https://doi.org/10.1016/j.aej.2024.12.109.
34. Sukhveer Singh Sandhu, Hamed Taheri Gorji, Pantea Tavakolian, Kouhyar Tavakolian, and Alireza Akhbardeh, “Medical Imaging
Applications of Federated Learning,” Diagnostics, vol. 13, no. 19, pp. 3140–3140, Oct. 2023, doi:
https://doi.org/10.3390/diagnostics13193140.
35. A. A. Lima, M. F. Mridha, S. C. Das, M. M. Kabir, Md. R. Islam, and Y. Watanobe, “A Comprehensive Survey on the Detection,
Classification, and Challenges of Neurological Disorders,” Biology, vol. 11, no. 3, p. 469, Mar. 2022, doi:
https://doi.org/10.3390/biology11030469.
36. T. White, E. Blok, and V. D. Calhoun, “Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges,
and monsters under the bed,” Human Brain Mapping, vol. 43, no. 1, Jul. 2020, doi: https://doi.org/10.1002/hbm.25120.
37. M. Mohammadi et al., “Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications,” npj Digital
Medicine, Jan. 2026, doi: https://doi.org/10.1038/s41746-025-02280-z.
38. G. Ganapathy, Sujatha Jamuna Anand, M. Jayaprakash, S. Lakshmi, V.Banu Priya, and Samuthira Pandi, “A BLOCKCHAIN BASED
FEDERATED DEEP LEARNING MODEL FOR SECURED DATA TRANSMISSION IN HEALTHCARE IOT NETWORKS,”
Measurement Sensors, pp. 101176–101176, May 2024, doi: https://doi.org/10.1016/j.measen.2024.101176.
39. R. Azzi, R. K. Chamoun, A. Serhrouchni, and M. Sokhn, “Unlocking the Potential of Data: Toward a Secure and Privacy-Preserving
Blockchain-Based Health Data Governance Framework,” Blockchain Research and Applications, pp. 100318–100318, Jun. 2025, doi:
https://doi.org/10.1016/j.bcra.2025.100318.
40. S. Wassan, Liudajun, H. Ying, H. Dongyan, and P. Fei, “Federated learning and differential privacy: Machine learning and deep learning
for biomedical image data classification,” DIGITAL HEALTH, vol. 11, May 2025, doi: https://doi.org/10.1177/20552076251358531.
41. X. Li, “Functional, Structural, and AI-Based MRI Analysis: A Comprehensive Review of Recent Advances,” Diagnostics, vol. 15, no.
24, p. 3212, Dec. 2025, doi: https://doi.org/10.3390/diagnostics15243212.
42. Prayitno et al., “A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and
Applications,” Applied Sciences, vol. 11, no. 23, p. 11191, Nov. 2021, doi: https://doi.org/10.3390/app112311191.
Non-Generative AI Basics,” Modern Pathology, vol. 38, no. 4, p. 100688, Jan. 2025, doi: https://doi.org/10.1016/j.modpat.2024.100688.
2. P. L. Chong et al., “Integrating artificial intelligence in healthcare: applications, challenges, and future directions,” Future Science OA,
vol. 11, no. 1, Jul. 2025, doi: https://doi.org/10.1080/20565623.2025.25275053. Y. Webb-Vargas et al., “Big Data and Neuroimaging,” Statistics in Biosciences, vol. 9, no. 2, pp. 543–558, May 2017, doi:
https://doi.org/10.1007/s12561-017-9195-y.
4. Georgios Drainakis, P. Pantazopoulos, Κωνσταντίνος Κατσαρός, Vasilis Sourlas, Angelos Amditis, and D. I. Kaklamani, “From
centralized to Federated Learning: Exploring performance and end-to-end resource consumption,” Computer Networks, vol. 225, pp.
109657–109657, Apr. 2023, doi: https://doi.org/10.1016/j.comnet.2023.109657.
5. Betul Yurdem, Murat Kuzlu, Mehmet Kemal Gullu, Ferhat Ozgur Catak, and M. Tabassum, “Federated Learning: Overview, Strategies,
Applications, Tools and Future Directions,” Heliyon, vol. 10, no. 19, pp. e38137–e38137, Sep. 2024, doi:
https://doi.org/10.1016/j.heliyon.2024.e38137.
6. K. Rootes-Murdy et al., “Federated Analysis of Neuroimaging Data: A Review of the Field,” Neuroinformatics, vol. 20, no. 2, pp. 377–
390, Nov. 2021, doi: https://doi.org/10.1007/s12021-021-09550-7.
7. J.-P. A. Yaacoub, H. N. Noura, and O. Salman, “Security of federated learning with IoT systems: Issues, limitations, challenges, and
solutions,” Internet of Things and Cyber-Physical Systems, vol. 3, Apr. 2023, doi: https://doi.org/10.1016/j.iotcps.2023.04.001.
8. M. S and J. K R, “Blockchain-IoMT-enabled federated learning: An intelligent privacy-preserving control policy for electronic health
records,” Array, vol. 28, p. 100586, Dec. 2025, doi: https://doi.org/10.1016/j.array.2025.100586.
9. C. Yen, C.-L. Lin, and M.-C. Chiang, “Exploring the frontiers of neuroimaging: A review of recent advances in understanding brain
functioning and disorders,” Life, vol. 13, no. 7, pp. 1–27, Jul. 2023, doi: https://doi.org/10.3390/life13071472.
10. L. A. Whitten, Functional Magnetic Resonance Imaging (fMRI): An Invaluable Tool in Translational Neuroscience. Research Triangle
Park (NC): RTI Press, 2012. Available: https://www.ncbi.nlm.nih.gov/books/NBK538909/
11. Y. Sun, W. Chen, and Z. Sun, “Multi-modal learning methods in medical imaging area: A survey,” Digital Signal Processing, vol. 167,
p. 105441, Dec. 2025, doi: https://doi.org/10.1016/j.dsp.2025.105441.
12. K. V. Sarma et al., “Federated learning improves site performance in multicenter deep learning without data sharing,” Journal of the
American Medical Informatics Association: JAMIA, vol. 28, no. 6, pp. 1259–1264, Jun. 2021, doi:
https://doi.org/10.1093/jamia/ocaa341.
13. S. R. Abbas, Z. Abbas, A. Zahir, and S. W. Lee, “Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy,
Security, and Predictive Analytics with IoT Integration,” Healthcare, vol. 12, no. 24, pp. 2587–2587, Dec. 2024, doi:
https://doi.org/10.3390/healthcare12242587.
14. D. R. Roalf and R. C. Gur, “Functional brain imaging in neuropsychology over the past 25 years.,” Neuropsychology, vol. 31, no. 8, pp.
954–971, Nov. 2017, doi: https://doi.org/10.1037/neu0000426.
15. F. M. AbdelSalam, “Blockchain Revolutionizing Healthcare Industry: A Systematic Review of Blockchain Technology Benefits and
Threats,” Perspectives in Health Information Management, vol. 20, no. 3, p. 1b, Sep. 2023, Available:
https://pmc.ncbi.nlm.nih.gov/articles/PMC10701638/
16. J. Andrew, D. P. Isravel, K. M. Sagayam, B. Bhushan, Y. Sei, and J. Eunice, “Blockchain for Healthcare systems: Architecture, Security
challenges, Trends and Future Directions,” Journal of Network and Computer Applications, vol. 215, pp. 103633–103633, Jun. 2023,
doi: https://doi.org/10.1016/j.jnca.2023.103633.
17. Y. Chang, C. Fang, and W. Sun, “A Blockchain-Based Federated Learning Method for Smart Healthcare,” Computational Intelligence
and Neuroscience, vol. 2021, pp. 1–12, Nov. 2021, doi: https://doi.org/10.1155/2021/4376418.
18. S. Paul, A. Mitra, S. Ghosh, and A. Podder, “Federated Learning for Decentralized Healthcare: Privacy, Efficiency, and Scalability in
Healthcare 5.0,” Studies in Computational Intelligence, pp. 427–444, 2026, doi: https://doi.org/10.1007/978-3-032-03985-9_19.
19. G. Ciaburro, “Benefits and use of blockchain technology to support supply chain during COVID-19,” Lessons from COVID-19, pp.
171–211, 2022, doi: https://doi.org/10.1016/b978-0-323-99878-9.00003-0.
20. S. S. Sefati, R. Craciunescu, B. Arasteh, S. Halunga, O. Fratu, and I. Tal, “Cybersecurity in a Scalable Smart City Framework Using
Blockchain and Federated Learning for Internet of Things (IoT),” Smart Cities, vol. 7, no. 5, pp. 2802–2841, Oct. 2024, doi:
https://doi.org/10.3390/smartcities7050109.
21. W. Ning et al., “Blockchain-Based Federated Learning: A Survey and New Perspectives,” Applied Sciences, vol. 14, no. 20, pp. 9459–
9459, Oct. 2024, doi: https://doi.org/10.3390/app14209459.
22. M. M. Ibrahimy, A. Norta, and P. Normak, “Blockchain-Based Governance Models Supporting Corruption-Transparency: A Systematic
Literature Review,” Blockchain: Research and Applications, vol. 5, no. 2, p. 100186, Dec. 2023, doi:
https://doi.org/10.1016/j.bcra.2023.100186.
23. J. Huang, K. Zhou, A. Xiong, and D. Li, “Smart Contract Vulnerability Detection Model Based on Multi-Task Learning,” Sensors, vol.
22, no. 5, p. 1829, Feb. 2022, doi: https://doi.org/10.3390/s22051829.
24. N. M. Singh et al., “How Machine Learning is Powering Neuroimaging to Improve Brain Health,” Neuroinformatics, Mar. 2022, doi:
https://doi.org/10.1007/s12021-022-09572-9.
25. T. Henry, L. Assekour, A. Rapetti, A. Del Pozzo, and S. Tucci-Piergiovanni, “Secrets on the Chain: Cryptographic Blockchain Patterns
for Verifiable and Confidential Data Handling,” Blockchain: Research and Applications, p. 100422, Dec. 2025, doi:
https://doi.org/10.1016/j.bcra.2025.100422.
26. Y. Tang et al., “A Survey on Blockchain-Based Federated Learning: Categorization, Application and Analysis,” Cmes-computer
Modeling in Engineering & Sciences, vol. 139, no. 3, pp. 2451–2477, Jan. 2024, doi: https://doi.org/10.32604/cmes.2024.030084.
27. Z. L. Teo et al., “Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture,”
Cell Reports Medicine, vol. 5, no. 2, p. 101419, Feb. 2024, doi: https://doi.org/10.1016/j.xcrm.2024.101419.
28. M. A. Sahid, M. P. Uddin, H. Saha, and M. R. Islam, “Towards privacy-preserving Alzheimer’s disease classification: Federated
learning on T1-weighted magnetic resonance imaging data,” DIGITAL HEALTH, vol. 10, Jan. 2024, doi:
https://doi.org/10.1177/20552076241295577.
29. A. Kouli, K. M. Torsney, and W.-L. Kuan, “Parkinson’s Disease: Etiology, Neuropathology, and Pathogenesis,” Parkinson’s Disease:
Pathogenesis and Clinical Aspects, vol. 1, no. 1, pp. 3–26, 2018, doi:
https://doi.org/10.15586/codonpublications.parkinsonsdisease.2018.ch1.
30. Mosleh Hmoud Al-Adhaileh, S. Ahmad, A. A. Alharbi, M. Alarfaj, Mukta Dhopeshwarkar, and Theyazn H H Aldhyani, “Diagnosis of
epileptic seizure neurological condition using EEG signal: a multi-model algorithm,” Frontiers in Medicine, vol. 12, May 2025, doi:
https://doi.org/10.3389/fmed.2025.1577474.
31. K. Ghamdi and J. AlMusailhi, “Attention-deficit Hyperactivity Disorder and Autism Spectrum Disorder: Towards Better Diagnosis andManagement,” Medicinski arhiv, vol. 78, no. 2, pp. 159–159, Jan. 2024, doi: https://doi.org/10.5455/medarh.2024.78.159-163.
32. P. Ratta, A. Kaur, S. Sharma, M. Shabaz, and G. Dhiman, “Application of Blockchain and Internet of Things in Healthcare and Medical
Sector: Applications, Challenges, and Future Perspectives,” Journal of Food Quality, vol. 2021, pp. 1–20, May 2021, doi:
https://doi.org/10.1155/2021/7608296.
33. Z. Guo, “Blockchain-enhanced smart contracts for formal verification of IoT access control mechanisms,” Alexandria Engineering
Journal, vol. 118, pp. 315–324, Apr. 2025, doi: https://doi.org/10.1016/j.aej.2024.12.109.
34. Sukhveer Singh Sandhu, Hamed Taheri Gorji, Pantea Tavakolian, Kouhyar Tavakolian, and Alireza Akhbardeh, “Medical Imaging
Applications of Federated Learning,” Diagnostics, vol. 13, no. 19, pp. 3140–3140, Oct. 2023, doi:
https://doi.org/10.3390/diagnostics13193140.
35. A. A. Lima, M. F. Mridha, S. C. Das, M. M. Kabir, Md. R. Islam, and Y. Watanobe, “A Comprehensive Survey on the Detection,
Classification, and Challenges of Neurological Disorders,” Biology, vol. 11, no. 3, p. 469, Mar. 2022, doi:
https://doi.org/10.3390/biology11030469.
36. T. White, E. Blok, and V. D. Calhoun, “Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges,
and monsters under the bed,” Human Brain Mapping, vol. 43, no. 1, Jul. 2020, doi: https://doi.org/10.1002/hbm.25120.
37. M. Mohammadi et al., “Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications,” npj Digital
Medicine, Jan. 2026, doi: https://doi.org/10.1038/s41746-025-02280-z.
38. G. Ganapathy, Sujatha Jamuna Anand, M. Jayaprakash, S. Lakshmi, V.Banu Priya, and Samuthira Pandi, “A BLOCKCHAIN BASED
FEDERATED DEEP LEARNING MODEL FOR SECURED DATA TRANSMISSION IN HEALTHCARE IOT NETWORKS,”
Measurement Sensors, pp. 101176–101176, May 2024, doi: https://doi.org/10.1016/j.measen.2024.101176.
39. R. Azzi, R. K. Chamoun, A. Serhrouchni, and M. Sokhn, “Unlocking the Potential of Data: Toward a Secure and Privacy-Preserving
Blockchain-Based Health Data Governance Framework,” Blockchain Research and Applications, pp. 100318–100318, Jun. 2025, doi:
https://doi.org/10.1016/j.bcra.2025.100318.
40. S. Wassan, Liudajun, H. Ying, H. Dongyan, and P. Fei, “Federated learning and differential privacy: Machine learning and deep learning
for biomedical image data classification,” DIGITAL HEALTH, vol. 11, May 2025, doi: https://doi.org/10.1177/20552076251358531.
41. X. Li, “Functional, Structural, and AI-Based MRI Analysis: A Comprehensive Review of Recent Advances,” Diagnostics, vol. 15, no.
24, p. 3212, Dec. 2025, doi: https://doi.org/10.3390/diagnostics15243212.
42. Prayitno et al., “A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and
Applications,” Applied Sciences, vol. 11, no. 23, p. 11191, Nov. 2021, doi: https://doi.org/10.3390/app112311191.
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