Storage of Developed Distributed Machine Learning Models on Blockchain

Abstract

In this study, a data set containing air pollution information collected from different locations was used. This dataset contains air pollution measurement information and results information for 25 different districts of Seoul city. The values obtained from similar regions were filtered from the entire data set and a data set specific to these regions was created. As the next step, the model was trained with the values obtained in its own regions and the model was recorded. When this model is tested with the data obtained in its own fields, it shows very successful results. However, when tested with data obtained from different regions and dissimilar to these regions, the model classification success remained very low. The aim of the study is to show that more successful results can be obtained with the distributed learning method, which can be a solution to this problem situation, compared to classical machine learning. In addition, security problems during model aggregation and aggregation should not be ignored in the Distributed Learning section. For this reason, a robust blockchain-based approach is proposed against malicious attacks during model deployment.

Keywords

• Distributed learning
• machine learning
• blockchain
• secure storage of model

Geliştirilen Dağıtılmış Makine Öğrenimi Modellerinin Blok Zincirde Depolanması

Abstract

Bu çalışmada farklı lokasyonlardan toplanan hava kirliliği bilgilerini içeren bir veri seti kullanılmıştır. Bu veri seti Seul şehrinin 25 farklı ilçesine ait hava kirliliği ölçüm bilgileri ve sonuç bilgilerini içermektedir. Benzer bölgelerden elde edilen değerler tüm veri seti içerisinden filtrelenerek bu bölgelere has veri seti oluşturulmuştur. Sonraki işlem basamağı olarak, kendi bölgelerinde elde edilen değerler ile eğitilip model kaydedilmiştir. Bu model kendi alanlarında elde edilen veriler ile test edildiğinde oldukça başarılı sonuçlar ortaya koymaktadır. Fakat farklı bölgelerden elde edilen ve bu bölgeler ile benzeşmeyen veriler ile test edildiğinde model sınıflama başarısı oldukça düşük kalmıştır. Çalışmanın amacı bu problem durumuna çözüm olabilecek olan dağıtık öğrenme yöntemi ile klasik makine öğrenmesine kıyasla daha başarılı sonuçlar elde edilebileceğini göstermektir. Bunun yanında Dağıtık Öğrenme kısmında model birleştirme ve toplama esnasındaki güvenlik sorunlarının da göz ardı edilmemelidir. Bu sebeple model dağıtımı sırasında kötü niyetli saldırılara karşı blockchain tabanlı sağlam bir yaklaşım önerilmiştir.

Keywords

• Dağıtık öğrenme
• makine öğrenmesi
• blok zinciri
• modelin güvenli depolanması

References

1. [1] O. Güler and S. Savaş, “All aspects of metaverse studies, technologies and future,” Gazi Journal of Engineering Sciences, vol. 8, no. 2, pp. 292–319, Sep. 2022. doi:10.30855/gmbd.0705011
2. [2] E. A. Çubukçu, V. Demir and M. F. Sevimli, “Estimating streamflow data with machine learning techniques,” Gazi Journal of Engineering Sciences, vol. 8, no. 2, pp. 257–272, Sep. 2022. doi:10.30855/gmbd.0705009
3. [3] K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via over-the-air computation,” IEEE Trans Wirel Commun, vol. 19, no. 3, pp. 2022–2035, Mar. 2020. doi:10.1109/TWC.2019.2961673
4. [4] T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: challenges, methods, and future directions,” IEEE Signal Process Mag, vol. 37, no. 3, pp. 50–60, May 2020. doi:10.1109/MSP.2020.2975749
5. [5] Cao, X., Başar, T., Diggavi, S., Eldar, Y. C., Letaief, K. B., Poor, H. V., & Zhang, J. ., “Communication-efficient distributed learning: an overview,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 4, pp. 851–873, Apr. 2023. doi:10.1109/JSAC.2023.3242710
6. [6] Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., He, B., “A survey on federated learning systems: vision, hype and reality for data privacy and protection,” IEEE Trans Knowl Data Eng, Apr. 2021. doi:10.1109/TKDE.2021.3124599
7. [7] C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao, “A survey on federated learning,” Knowl Based Syst, vol. 216, p. 106775, Mar. 2021. doi:10.1016/J.KNOSYS.2021.106775
8. [8] J. P. Albrecht, “How the GDPR Will Change the World,” European Data Protection Law Review (EDPL), vol. 2, 2016, Accessed: Jun. 20, 2023. [Online]. Available: https://heinonline.org/HOL/Page?handle=hein.journals/edpl2&id=313&div=&collection=

9. [9] X. Wang, W. Qin, F. Jiao, L. Dong, J. Guo, J. Zhang and C. Yang, “Review of tungsten resource reserves, tungsten concentrate production and tungsten beneficiation technology in China,” Transactions of Nonferrous Metals Society of China, vol. 32, no. 7, pp. 2318–2338, Jul. 2022. doi:10.1016/S1003-6326(22)65950-8
10. [10] S. Raschka, “MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack,” J Open Source Softw, vol. 3, no. 24, p. 638, Apr. 2018. doi:10.21105/joss.00638
11. [11] A. Gaier and D. Ha, “Weight agnostic neural networks,” Adv Neural Inf Process Syst, vol. 32, 2019, Accessed: Jun. 20, 2023. [Online]. Available: https://weightagnostic.github.io/
12. [12] E. Tijan, S. Aksentijević, K. Ivanić and M. Jardas, “Blockchain technology ımplementation in logistics,” Sustainability 2019, Vol. 11, Page 1185, vol. 11, no. 4, p. 1185, Feb. 2019. doi:10.3390/SU11041185
13. [13] J. Golosova and A. Romanovs, “The advantages and disadvantages of the blockchain technology,” 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering, AIEEE 2018 - Proceedings, Dec. 2018. doi:10.1109/AIEEE.2018.8592253
14. [14] Z. Fauziah, H. Latifah, X. Omar, A. Khoirunisa and S. Millah, “Application of blockchain technology in smart contracts: a systematic literature review,” Aptisi Transactions on Technopreneurship (ATT), vol. 2, no. 2, pp. 160–166, Aug. 2020. doi:10.34306/ATT.V2I2.97
15. [15] “A Blockchain Platform for the Enterprise — hyperledger-fabricdocs main documentation.” https://hyperledger-fabric.readthedocs.io/en/release-2.5/ (accessed Jun. 20, 2023).
16. [16] “Blockchain Technology Projects – Hyperledger Foundation.” https://www.hyperledger.org/use (accessed Jun. 20, 2023).
17. [17] Q. Nasir, I. A. Qasse, M. Abu Talib and A. B. Nassif, “Performance analysis of hyperledger fabric platforms,” Security and Communication Networks, vol. 2018, 2018. doi:10.1155/2018/3976093
18. [18] S. Tanwar, Q. Bhatia, P. Patel, A. Kumari, P. K. Singh and W. C. Hong, “Machine learning adoption in blockchain-based smart applications: the challenges, and a way forward,” IEEE Access, vol. 8, pp. 474–448, 2020. doi:10.1109/ACCESS.2019.2961372
19. [19] Y. Liu, F. R. Yu, X. Li, H. Ji and V. C. M. Leung, “Blockchain and machine learning for communications and networking systems,” IEEE Communications Surveys and Tutorials, vol. 22, no. 2, pp. 1392–1431, Apr. 2020. doi:10.1109/COMST.2020.2975911
20. [20] A. Adhikari, D. B. Rawat and M. Song, “Wireless network virtualization by leveraging blockchain technology and machine learning,” WiseML 2019 - Proceedings of the 2019 ACM Workshop on Wireless Security and Machine Learning, pp. 61–66, May 2019. doi:10.1145/3324921.3328790
21. [21] R. Shinde, O. Nilakhe, P. Pondkule, D. Karche and P. Shendage, “Enhanced Road Construction Process with Machine Learning and Blockchain Technology,” 2020 International Conference on Industry 4.0 Technology, I4Tech 2020, pp. 207–210, Feb. 2020. doi:10.1109/I4TECH48345.2020.9102669
22. [22] M. U. Nasir, S. Khan, S. Mehmood, M. A. Khan, M. Zubair and S. O. Hwang, “Network meddling detection using machine learning empowered with blockchain technology,” Sensors 2022, Vol. 22, Page 6755, vol. 22, no. 18, p. 6755, Sep. 2022. doi:10.3390/S22186755
23. [23] T. Wang, “A unified analytical framework for trustable machine learning and automation running with blockchain,” Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, pp. 4974–4983, Jan. 2019. doi:10.1109/BIGDATA.2018.8622262
24. [24] Z. A. El Houda, A. Hafid and L. Khoukhi, “BrainChain - A machine learning approach for protecting blockchain applications using SDN,” IEEE International Conference on Communications, vol. 2020-June, Jun. 2020. doi:10.1109/ICC40277.2020.9148808
25. [25] W. Xiong and L. Xiong, “Smart contract based data trading mode using blockchain and machine learning,” IEEE Access, vol. 7, pp. 102331–102344, 2019. doi:10.1109/ACCESS.2019.2928325
26. [26] Z. Shahbazi and Y. C. Byun, “Improving transactional data system based on an edge computing–blockchain–machine learning ıntegrated framework,” Processes 2021, Vol. 9, Page 92, vol. 9, no. 1, p. 92, Jan. 2021. doi:10.3390/PR9010092
27. [27] N. V. Pardakhe and V. M. Deshmukh, “Machine learning and blockchain techniques used in healthcare system,” 2019 IEEE Pune Section International Conference, PuneCon 2019, Dec. 2019. doi:10.1109/PUNECON46936.2019.9105710
28. [28] Khan, M. A., Abbas, S., Rehman, A., Saeed, Y., Zeb, A., Uddin, M. I., Nasser, N., Ali, A, “A machine learning approach for blockchain-based smart home networks security,” IEEE Netw, vol. 35, no. 3, pp. 223–229, May 2021. doi:10.1109/MNET.011.2000514
29. [29] F. Jamil, H. K. Kahng, S. Kim and D. H. Kim, “Towards secure fitness framework based on ıot-enabled blockchain network ıntegrated with machine learning algorithms,” Sensors 2021, Vol. 21, Page 1640, vol. 21, no. 5, p. 1640, Feb. 2021. doi:10.3390/S21051640
30. [30] Uddin, M., Memon, M. S., Memon, I., Ali, I., Memon, J., Abdelhaq, M., & Alsaqour, R., “Hyperledger fabric blockchain: secure and efficient solution for electronic health records,” Computers, Materials & Continua, vol. 68, no. 2, pp. 2377–2397, 2021. doi:10.32604/cmc.2021.015354
31. [31] F. P. Oikonomou, J. Ribeiro, G. Mantas, J. M. C. S. Bastos and J. Rodriguez, “A hyperledger fabric-based blockchain architecture to secure ıot-based health monitoring systems,” 2021 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2021, pp. 186–190, 2021. doi:10.1109/MEDITCOM49071.2021.9647521
32. [32] N. Lu, Y. Zhang, W. Shi, S. Kumari and K. K. R. Choo, “A secure and scalable data integrity auditing scheme based on hyperledger fabric,” Comput Secur, vol. 92, p. 101741, May 2020. doi:10.1016/J.COSE.2020.101741