Abstract
As the domains of artificial intelligence (AI) and blockchain technology continue to evolve, there is a growing interest in exploring their intersection and potential synergies. This paper provides a comprehensive review of recent advancements and applications of AI in the context of blockchain technology. In this work, we analyze the utilization of blockchain technology to enhance AI algorithms, facilitate decentralized AI systems, and address challenges related to data privacy, security, and scalability. Additionally, we discuss emerging trends, challenges, and future research directions in the field of AI in blockchain, highlighting opportunities for innovation and collaboration.
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References
Aknan, M., Singh, M. P., & Arya, R. (2023). AI and Blockchain Assisted Framework for Offloading and Resource Allocation in Fog Computing. Journal of Grid Computing, 21(4). https://doi.org/10.1007/s10723-023-09694-7
Alatawi, M. N. (2023). An Approach Based on Machine Learning for the Cybersecurity of Blockchain-Based Smart Internet of Medical Things (IoMT) Networks. International Journal of Software Engineering and Knowledge Engineering, 33(10), 1513–1535. https://doi.org/10.1142/S0218194023500419
Assiri, F. Y., & Ragab, M. (2023). Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment. Mathematics, 11(19). https://doi.org/10.3390/math11194080
Baldominos, A., & Saez, Y. (2019). Coin.AI: A proof-of-useful-work scheme for blockchain-based distributed deep learning. Entropy, 21(8), 723.
Bian, G., Qu, W., & Shao, B. (2023). Blockchain-Based Trusted Federated Learning with Pre-Trained Models for COVID-19 Detection. Electronics (Switzerland), 12(9). https://doi.org/10.3390/electronics12092068
Bravo-Marquez, F., Reeves, S., & Ugarte, M. (2019, April). Proof-of-learning: a blockchain consensus mechanism based on machine learning competitions. In 2019 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPCON) (pp. 119–124). IEEE.
Chamola, V., Goyal, A., Sharma, P., Hassija, V., Binh, H. T. T., & Saxena, V. (2023). Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management. Neural Computing and Applications, 35(31), 22959–22969. https://doi.org/10.1007/s00521-022-07087-7
Chenli, C., Li, B., Shi, Y., & Jung, T. (2019, May). Energy-recycling blockchain with proof-of-deep-learning. In 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (pp. 19–23). IEEE.
Chi, C., Yin, Z., Liu, Y., & Chai, S. (2024). A Trusted Cloud-Edge Decision Architecture Based on Blockchain and MLP for AIoT. IEEE Internet of Things Journal, 11(1), 201–216. https://doi.org/10.1109/JIOT.2023.3300845
Golec, M., Gill, S. S., Golec, M., Xu, M., Ghosh, S. K., Kanhere, S. S., Rana, O., & Uhlig, S. (2023). BlockFaaS: Blockchain-enabled Serverless Computing Framework for AI-driven IoT Healthcare Applications. Journal of Grid Computing, 21(4). https://doi.org/10.1007/s10723-023-09691-w
Harris, J. D. (2020, September). Analysis of Models for Decentralized and Collaborative AI on Blockchain. In International Conference on Blockchain (pp. 142–153). Springer, Cham.
Harris, J. D., & Waggoner, B. (2019, July). Decentralized and collaborative AI on blockchain. In 2019 IEEE International Conference on Blockchain (Blockchain) (pp. 368–375). IEEE.
Krichen, M. (2023). Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence. Computers, 12(5). https://doi.org/10.3390/computers12050107
Kumar, S., Lim, W. M., Sivarajah, U., & Kaur, J. (2023). Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis. Information Systems Frontiers, 25(2), 871–896. https://doi.org/10.1007/s10796-022-10279-0
Lan, Y., Liu, Y., & Li, B. (2020). Proof of Learning (PoLe): Empowering Machine Learning with Consensus Building on Blockchains. arXiv preprint arXiv:2007.15145.
Li, B., Chenli, C., Xu, X., Shi, Y., & Jung, T. (2019). DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services. arXiv preprint arXiv:1904.07349.
Li, J., Qin, R., Guan, S., Hou, J., & Wang, F. (2024). Blockchain Intelligence: Intelligent Blockchains for Web 3.0 and Beyond. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1–10. https://doi.org/10.1109/TSMC.2023.3348449
Merlina, A. (2019, December). BlockML: a useful proof of work system based on machine learning tasks. In Proceedings of the 20th International Middleware Conference Doctoral Symposium (pp. 6–8).
Nath, P., Mushahary, J. R., Roy, U., Brahma, M., & Singh, P. K. (2023). AI and Blockchain-based source code vulnerability detection and prevention system for multiparty software development. Computers and Electrical Engineering, 106. https://doi.org/10.1016/j.compeleceng.2023.108607
Pandl, K. D., Thiebes, S., Schmidt-Kraepelin, M., & Sunyaev, A. (2020). On the convergence of artificial intelligence and distributed ledger technology: A scoping review and future research agenda. IEEE Access, 8, 57075–57095.
Sami, H., Mizouni, R., Otrok, H., Singh, S., Bentahar, J., & Mourad, A. (2024). LearnChain: Transparent and cooperative reinforcement learning on Blockchain. Future Generation Computer Systems, 150, 255–271. https://doi.org/10.1016/j.future.2023.09.012
Shinde, R., Patil, S., Kotecha, K., Potdar, V., Selvachandran, G., & Abraham, A. (2024). Securing AI-based healthcare systems using blockchain technology: A state-of-the-art systematic literature review and future research directions. Transactions on Emerging Telecommunications Technologies, 35(1). https://doi.org/10.1002/ett.4884
Shreya, S., Chatterjee, K., & Singh, A. (2023). BFSF: A secure IoT based framework for smart farming using blockchain. Sustainable Computing: Informatics and Systems, 40. https://doi.org/10.1016/j.suscom.2023.100917
Sinha, A., Singh, S., & Verma, H. K. (2024). AI-Driven Task Scheduling Strategy with Blockchain Integration for Edge Computing. Journal of Grid Computing, 22(1). https://doi.org/10.1007/s10723-024-09743-9
Sun, C., Li, D., Wang, B., & Song, J. (2023). AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle. Symmetry, 15(6). https://doi.org/10.3390/sym15061264
Swathi, G., & Pahuja, A. (2024). FinTech Frontiers: Cloud Computing and Artificial Intelligence Applications for Intelligent Finance Investment and Blockchain in the Financial Sector. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 654–659. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179664161&partnerID=40&md5=cd23c36956e0fb05e1aa0227cfb50ecf
Yan, X., Xu, Y., Yao, S., & Sun, Y. (2023). A Domain Embedding Model for Botnet Detection Based on Smart Blockchain. IEEE Internet of Things Journal, 1. https://doi.org/10.1109/JIOT.2023.3320046
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Vubangsi, M., Nyuga, G., Al-Turjman, F. (2024). Exploring the Intersection of Artificial Intelligence and Blockchain Technology in Complex Systems: A Systematic Review. In: Al-Turjman, F. (eds) The Smart IoT Blueprint: Engineering a Connected Future. AIoTSS 2024. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-63103-0_25
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