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Enhanced DASS-CARE 2.0: a blockchain-based and decentralized FL framework

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Abstract

The emergence of the Cognitive Internet of Medical Things (CIoMT) during the COVID-19 pandemic has been transformational. The CIoMT is a rapidly evolving technology that uses artificial intelligence, big data, and the Internet of Things (IoT) to provide personalized patient care. The CIoMT can be used to monitor and track vital signs, such as temperature, blood pressure, and heart rate, thus giving healthcare providers real-time information about a patient’s health. However, in such systems, the problem of privacy during data processing or sharing remains. Therefore, federated learning (FL) plays an important role in the Cognitive Internet of Medical Things (CIoMT) by allowing multiple medical devices to securely collaborate in a distributed and privacy-preserving manner. On the other hand, classical centralized FL models have several limitations, such as single points of failure and malicious servers. This paper presents an enhancement of the existing DASS-CARE 2.0 framework by using a blockchain-based federated learning framework. The proposed solution provides a secure and reliable distributed learning platform for medical data sharing and analytics in a multi-organizational environment. The blockchain-based federated learning framework offrs an innovative solution to overcome the challenges encountered in traditional FL. Furthermore, we provide a comprehensive discussion of the advantages of the proposed framework through a comparative study between our DASS-CARE 2.0 and the traditional centralized FL model while taking the aforementioned security challenges into consideration. Overall, the performance of the proposed framework shows significant advantages compared to traditional methods.

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Notes

  1. https://neurokit2.readthedocs.io/en/legacy_docs/

  2. https://github.com/jxx123/simglucose

  3. www.marwan.ma/hpc

References

  1. Ciotti M, Ciccozzi M, Terrinoni A, Jiang W-C, Wang C-B, Bernardini S (2020) The covid-19 pandemic. Critical reviews in clinical laboratory sciences 57(6):365–388

    Article  Google Scholar 

  2. Swayamsiddha S, Mohanty C (2020) Application of cognitive internet of medical things for covid-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14(5):911–915

    Article  Google Scholar 

  3. Pan XB (2020) Application of personal-oriented digital technology in preventing transmission of covid-19, china. Irish Journal of Medical Science (1971) 189(4):1145–1146

  4. Pavithran D, Shaalan K, Al-Karaki JN, Gawanmeh A (2020) Towards building a blockchain framework for iot. Cluster Computing 23(3):2089–2103

    Article  Google Scholar 

  5. Sood SK, Mahajan I (2017) Wearable iot sensor based healthcare system for identifying and controlling chikungunya virus. Computers in Industry 91:33–44

    Article  Google Scholar 

  6. Vaishya R, Javaid M, Khan IH, Haleem A (2020) Artificial intelligence (ai) applications for covid-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14(4):337–339

    Article  Google Scholar 

  7. Gawanmeh A, Al-Karaki JN (2021) Disruptive technologies for disruptive innovations Challenges and opportunities. ITNG 2021 18th International Conference on Information Technology-New Generations. Springer, Cham, pp 427–434

    Chapter  Google Scholar 

  8. Shailaja K, Seetharamulu B, Jabbar MA (2018) Machine learning in healthcare: A review. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), p 910–914

  9. Zhu H, Zhang H, Jin Y (2020) From federated learning to federated neural architecture search: A survey, 2020

  10. Zhang C, Xie Y, Bai H, Yu B, Li W, Gao Y (2021) A survey on federated learning. Knowledge-Based Systems 216:106775

    Article  Google Scholar 

  11. Kavalionak H, Carlini E, Dazzi P, Ferrucci L, Mordacchini M, Coppola M (2022) Decentralized federated learning and network topologies: an empirical study on convergence. In SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp 317-324, Tirrenia, Pisa, Italy, 19-22/06/2022

  12. Hosseini Bamakan SM, Motavali A, Bondarti AB (2020) A survey of blockchain consensus algorithms performance evaluation criteria. Expert Systems with Applications 154:113385

    Article  Google Scholar 

  13. Ayache M, Gawanmeh A, Al-Karaki JN (2022) Dass-care 2.0: Blockchain-based healthcare framework for collaborative diagnosis in ciomt ecosystem In 2022 5th Conference on Cloud and Internet of Things CIoT p 40–47, IEEE

  14. Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong (2019) Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol., 10(2) jan 2019

  15. Lim Wei YB, Luong NC, Hoang DT, Jiao Y, Liang YC, Yang Q, Niyato D, Miao C (2020) Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials 22(3):2031–2063

    Article  Google Scholar 

  16. Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K et al (2020) The future of digital health with federated learning. NPJ digital medicine 3(1):1–7

    Article  Google Scholar 

  17. Pokhrel SR, Choi J (2020) A decentralized federated learning approach for connected autonomous vehicles, In 2020 IEEE Wireless Communications and Networking Conference Workshops(WCNCW), p 1–6, IEEE

  18. Nguyen DC, Pham QV, Pathirana PN, Ding M, Seneviratne A, Lin Z, Dobre O, Hwang WJ (2020) Federated learning for smart healthcare: A survey ACM Comput. Surv., 55(3) feb 2022

  19. Mothukuri V, Parizi RM, Pouriyeh S, Huang Y, Dehghantanha A, Srivastava G (2021) A survey on security and privacy of federated learning. Future Generation Computer Systems 115:619–640

    Article  Google Scholar 

  20. Ma X, Sun H, Hu RQ, Qian Y (2020) A new implementation of federated learning for privacy and security enhancement

  21. Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37(3):50–60

    Article  Google Scholar 

  22. Mammen PM (2021) Federated learning: opportunities and challenges arXiv preprint arXiv 2101:05428

    Google Scholar 

  23. Bagdasaryan E, Veit A, Hua Y, Estrin D, Shmatikov V (2020) How to backdoor federated learning In International Conference on Artificial Intelligence and Statistics, p 2938–2948. PMLR

  24. Blanchard P, El Mhamdi EM, Guerraoui R, Stainer J (2017) Machine learning with adversaries: Byzantine tolerant gradient descent, Advances in Neural Information Processing Systems, 30

  25. Lyu L, Yu H, Yang Q (2020) Threats to federated learning: A survey arXiv preprint arXiv, 2003.02133

  26. Berkel CV (2009) Multi-core for mobile phones. In: 2009 Design, Automation & Test in Europe Conference & Exhibition, p 1260–1265. IEEE

  27. Wang Z, Hu Q (2021) Blockchain-based federated learning: A comprehensive survey, arXiv preprint arXiv: 2110.02182

  28. Zhang W, Lu Q, Yu Q, Li Z, Liu Y, Lo SK, Chen S, Xu X, Zhu L (2020) Blockchain-based federated learning for device failure detection in industrial iot. IEEE Internet of Things Journal 8(7):5926–5937

    Article  Google Scholar 

  29. Zhao Y, Zhao J, Jiang L, Tan R, Niyato D, Li Z, Lyu L, Liu Y (2020) Privacy-preserving blockchain-based federated learning for iot devices. IEEE Internet of Things Journal 8(3):1817–1829

  30. 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

    Article  Google Scholar 

  31. Al-Karaki JN, Gawanmeh A, Ayache M, Mashaleh A (2019) Dass-care: a decentralized, accessible, scalable, and secure healthcare framework using blockchain. In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), p 330–335. IEEE

  32. Song J, Wang W, Gadekallu TR, Cao J, Liu J (2022) Eppda: An efficient privacy-preserving data aggregation federated learning scheme. IEEE Transactions on Network Science and Engineering

  33. Rizwan A, Ahmad R, Khan AN, Xu R, Kim DK (2023) Intelligent digital twin for federated learning in aiot networks. Internet of Things, p 100698

  34. Lin WL, Hsieh CH, Chen TS, Chen J, Lee JL, Chen WC (2021) Apply iot technology to practice a pandemic prevention body temperature measurement system: A case study of response measures for covid-19. International Journal of Distributed Sensor Networks 17(5):15501477211018126

    Article  Google Scholar 

  35. Hammad M, Maher A, Wang K, Jiang F, Amrani M (2018) Detection of abnormal heart conditions based on characteristics of ecg signals. Measurement 125:634–644

  36. Chadt A, Al-Hasani H (2020) Glucose transporters in adipose tissue, liver, and skeletal muscle in metabolic health and disease. Pflügers Archiv-European Journal of Physiology 472(9):1273–1298

  37. Hyndman RJ, Wang E, Laptev N (2015) Large-scale unusual time series detection. In: 2015 IEEE international conference on data mining workshop (ICDMW), p 1616–1619. IEEE

  38. Beutel DJ, Topal T, Mathur A, Qiu X, Parcollet T, de Gusmão PPB, Lane ND (2020) Flower: A friendly federated learning research framework. arXiv preprint arXiv:2007.14390

  39. Beauchamp MH, Hutchison JS, Lacroix J (2010) Traitement par hypothermie du traumatisme cránien grave de l’enfant: le pour et le contre. Réanimation, 19(7):665–670

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Acknowledgements

We would like to thank the Moroccan National Research and Education Network (MARWAN) for granting access to their high-performance computing infrastructure, where a large portion of the numerical simulations presented in this work were performed.

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Authors and Affiliations

  1. STRS Laboratory, RAISS Team, INPT, Rabat, Morocco

    Meryeme Ayache, Ikram El Asri, Mohamed Bellouch & Karim Tazzi

  2. College of CIS, Zayed University, P.O. Box 144534, Abu Dhabi, United Arab Emirates

    Jamal N. Al-Karaki

  3. CpE Department, College of Engineering, The Hashemite University, Zarga, Jordan

    Jamal N. Al-Karaki

  4. College of Engineering and IT, University of Dubai, Dubai, United Arab Emirates

    Amjad Gawanmeh

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  1. Meryeme Ayache
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  2. Ikram El Asri
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Correspondence to Meryeme Ayache.

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Ayache, M., El Asri, I., Al-Karaki, J.N. et al. Enhanced DASS-CARE 2.0: a blockchain-based and decentralized FL framework. Ann. Telecommun. 78, 703–715 (2023). https://doi.org/10.1007/s12243-023-00965-8

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  • DOI: https://doi.org/10.1007/s12243-023-00965-8

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