Blockchain Management and Federated Learning Adaptation on Healthcare Management System

Full Text (PDF, 616KB), PP.1-13

Views: 0 Downloads: 0


Safiye Turgay 1,*

1. Department of Industrial Engineering, Sakarya University, Sakarya, Turkey

* Corresponding author.


Received: 13 Apr. 2022 / Revised: 11 Jun. 2022 / Accepted: 6 Aug. 2022 / Published: 8 Oct. 2022

Index Terms

Blockchain management, federated learning, healthcare management, differential entropy approach, machine learning


Recently, health management systems have some troubles such as insufficient sharing of medical data, security problems of shared information, tampering and leaking of private data with data modeling probes and developing technology. Local learning is performed together with federated learning and differential entropy method to prevent the leakage of medical confidential information, so blockchain-based learning is preferred to completely eliminate the possibility of leakage while in global learning. Qualitative and quantitative analysis of information can be made with information entropy technology for the effective and maximum use of medical data in the local learning process. The blockchain is used the distributed network structure and inherent security features, at the same time information is treated as a whole, not as islands of data. All the way through this work, data sharing between medical systems can be encouraged, access records tampered with, and better support medical research and definitive medical treatment. The M/M/1 queue for the memory pool and M/M/C queue to combine integrated blockchains with a unified learning structure. With the proposed model, the number of transactions per block, mining of each block, learning time, index operations per second, number of memory pools, waiting time in the memory pool, number of unconfirmed transactions in the whole system, total number of transactions were examined.
Thanks to this study, the protection of the medical privacy information of the user during the service process and the autonomous management of the patient’s own medical data will benefit the protection of privacy within the scope of medical data sharing. Motivated by this, proposed a blockchain and federated learning-based data management system able to develop in next studies.

Cite This Paper

Safiye Turgay, "Blockchain Management and Federated Learning Adaptation on Healthcare Management System", International Journal of Intelligent Systems and Applications(IJISA), Vol.14, No.5, pp.1-13, 2022. DOI:10.5815/ijisa.2022.05.01


[1]C.E. Shannon, A mathematical theory of communication, Bell Syst. Tech. J., vol. 27, pp. 379-423, 623-656, July-Oct. 1948.
[2]F. Jamil, M.A.Iqbal, R.Amin, D.Kim, , Adaptive Thermal-Aware Routing Protocol forWireless Body Area Network. Electronics 8, 2019, 1–28.
[3]R. Kashyap, Applications ofWireless Sensor Networks in Healthcare, in: IoT andWSN Applications for Modern Agricultural Advancements: Emerging Research and Opportunities, IGI Global, 2020, pp. 8–40.
[4]Y. Qi, M. S. Hossain , J. Nie, X. Li Privacy-preserving blockchain-based federated learning for traffic, flow prediction, Future Generation Computer Systems 117, 2021, 328–337
[5]J. Qu, Blockchain in medical informatics, Journal of Industrial Information Integration,
[6]S. Shi, D. He , L. Li, N. Kumar , M. Khurram Khan, K. R. Choo, Applications of blockchain in ensuring the security and privacy of electronic health record systems: A survey, Computers & Security 97 (2020) 101966..
[7]P. Singh, M. Masud, M. S. Hossain, A. Kaur, Blockchain and homomorphic encryption-based privacy-preserving data aggregation model in smart grid, Computers and Electrical Engineering 93 (2021) 107209
[8]S. Alam, M. Shuaib, W. Z. Khan, S. Garg, G. Kaddoum,M. S. Hossain, Y. B. Zikria, Blockchain-based Initiatives: Current state and challenges, Computer Networks 198 (2021) 108395
[9]M. Ali , H. Karimipour, M. Tariq, Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges, computers & security 108 (2021)102355
[10]M. A. Rahman, M. S. Hossain, A. J. Showail, N.A. Alrajeh, M. F. Alhamid, A secure, private, and explainable IoHT framework to support sustainable health monitoring in a smart city, Sustainable Cities and Society 72 (2021) 103083
[11]D. Di, F. Maesa , P. Mori, Blockchain 3.0 applications survey, Journal of Parallel and Distributed Computing 138 (2020) 99–11
[12]Z. Xiao, X. Xu, H. Xing,F. Song, X. Wang, B. Zhao, A federated learning system with enhanced feature extraction for human activity recognition, Knowledge-Based Systems 229 (2021) 107338
[13]S. Mojtab, H. Bamakan, S. G. Moghaddam, S.D. Manshadi, Blockchain-enabled pharmaceutical cold chain: Applications, key challenges, and future trends, Journal of Cleaner Production 302 (2021) 127021
[14]D. C. Nguyen, P. N. Pathirana, M. Ding, A. Seneviratne, Blockchain for 5G and beyond networks: A state of the art survey Review, Journal of Network and Computer Applications 166 (2020) 102693
[15]S. Saxena, B. Bhushan, M. A. Ahad, Blockchain based solutions to secure IoT: Background, integration trends and a way forward, Journal of Network and Computer Applications 181 (2021) 103050
[16]R. Lim, R Madeira, Portugal Toward Semantic IoT Load Inference Attention Management for Facilitating Healthcare and Public Health Collaboration: A Survey, The 10th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH 2020) November 2-5, 2020, Procedia Computer Science 177 (2020) 371–378
[17]M.A. Uddin, A. Stranieri, I. Gondal, V. Balasubramanian, A Survey on the Adoption of Blockchain in IoT: Challenges and Solutions, Blockchain: Research and Applications, PII: S2096-7209(21)00001-4, DOI:
[18]S. Singh, P. K. Sharm, B. Yoon, M. Shojafar, G. H. Cho, I.H. Ra, Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city, Sustainable Cities and Society 63 (2020) 102364
[19]D. Połap, G. Srivastava, K. Yu, Agent architecture of an intelligent medical system based on federated learning and blockchain technology, Journal of Information Security and Applications 58 (2021) 102748
[20]R.S. Abdullah , M.A., Faizal, Block Chain: Cryptographic Method in Fourth Industrial Revolution, I. J. Computer Network and Information Security, 2018, 11, 9-17
[21]S.. Anwar, S. Anayat, S. Butt, S. Butt, M. Saad, Generation Analysis of Blockchain Technology: Bitcoin and Ethereum, I.J. Information Engineering and Electronic Business, 2020, 4, 30-39, DOI: 10.5815/ijcnis.2018.11.02
[22]N. Truong, K. Sun, S. Wang, F. Guitton, Y.K. Guo. "Privacy preservation in federated learning: An insightful survey from the GDPR perspective", Computers & Security, 2021, Nguyen Truong et al.: Preprint submitted to Elsevier
[23]Y. Lu, Xiaohong Huang, Yan Zhang, Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT, IEEE Transactions on Industrial Informatics DOI:10.1109/TII.2019.2942190, International Conference on Blockchain and Trustworthy Systems BlockSys 2020: Blockchain and Trustworthy Systems pp 112-125
[24]Y. Zhao, M. Cui, L. Zheng, R. Zhang, L.Meng, D. Gao, Y.Zhang. "Research on electronic medical record access control based on blockchain", International Journal of Distributed Sensor Networks, November 18, 2019
[25]L. Stockburger, G. Kokosioulis, A. Mukkamala, R. R.Mukkamala, M. Avital."Blockchain-Enabled Decentralized Identify Management: The Case of Self-Sovereign Identity in Public Transportation", Blockchain: Research and Applications, 26 May 2021, 100014
[26]K.Zhang, H Huang, S.Guo, X. Zhou, (2020). Blockchain-Based Participant Selection for Federated Learning. In: Zheng, Z., Dai, HN., Fu, X., Chen, B. (eds) Blockchain and Trustworthy Systems. BlockSys 2020. Communications in Computer and Information Science, vol 1267. Springer, Singapore.
[27]Y. Xinyi, Z. Yi and Y. He, "Technical Characteristics and Model of Blockchain," 2018 10th International Conference on Communication Software and Networks (ICCSN), 2018, pp. 562-566, doi: 10.1109/ICCSN.2018.8488289.
[28]S.Chen, X.Cai, X.Wang, Blockchain applications in PLM towards smart manufacturing. Int J Adv Manuf Technol (2021).
[29]Q.L.Li, J.-Y.Ma, Y.-X. Chang, Blockchain Queueing Theory, 2018. Available online: 1808.01795 (accessed on 20 January 2019).
[30]RA Memon, J.P. Li, J.Ahmed, Simulation Model for Blockchain Systems Using Queuing Theory. Electronics. 2019; 8(2):234.
[31]N.Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System. Available online: pdf (accessed on 20 January 2019)
[32]B.Biais, C.Bisiere, M.Bouvard, C.Casamatta, The Blockchain Folk Theorem, 2018. Available online: