Blockchain based Federated Learning Models Methods and Applications
DOI:
https://doi.org/10.5281/zenodo.12643107Keywords:
Federated Learning; Data Privacy Protection; Data Circulation; Blockchain TechnologyAbstract
This paper systematically discusses the application and development of federated learning in data privacy protection and data value sharing. With the rapid development of global information technology, especially the explosive growth of data from Internet of Things devices, data security and privacy protection are facing unprecedented challenges. This paper first analyzes the growth trend of global data volume and its importance to next generation technologies such as artificial intelligence technologies such as deep learning. Second, the paper provides an in-depth look at the impact of current data privacy regulations on data flows and value creation, particularly the EU's GDPR and China's Data Security and Personal Information Protection Law. Then, this paper introduces in detail federated learning, as a new distributed machine learning paradigm, which effectively solves the contradiction between existing data sharing and privacy protection by protecting individual data privacy and realizing global model collaborative construction. Finally, this paper discusses the combination of blockchain technology and federated learning, and proposes BeFL architecture as a new secure, decentralized and trusted federated learning system, which is expected to provide a comprehensive solution for large-scale data processing and value creation in multi-party scenarios. The research in this paper not only deepens the understanding of federation learning in theory, but also provides important reference and enlightenment for future research and application in related fields.
Downloads
References
Song, J., Cheng, Q., Bai, X., Jiang, W., & Su, G. (2024). LSTM-based deep learning model for financial market stock price prediction. Journal of Economic Theory and Business Management, 1(2), 43-50.
Ni, C., Zhang, C., Lu, W., Wang, H., & Wu, J. (2024). Enabling intelligent decision making and optimization in enterprises through data pipelines.
Lu, W., Ni, C., Wang, H., Wu, J., & Zhang, C. (2024). Machine learning-based automatic fault diagnosis method for operating systems.
Liang, P., Song, B., Zhan, X., Chen, Z., & Yuan, J. (2024). Automating the training and deployment of models in MLOps by integrating systems with machine learning. Appl. Comput. Eng., 67, 1–7. https://doi.org/10.54254/2755-2721/67/20240690.
Zhou, Y., Zhan, T., Wu, Y., Song, B., & Shi, C. (2024). RNA secondary structure prediction using transformer-based deep learning models. Appl. Comput. Eng., 64, 95–101. https://doi.org/10.54254/2755-2721/64/20241362.
Liu, B., Cai, G., Ling, Z., Qian, J., & Zhang, Q. Precise positioning and prediction system for autonomous driving based on generative artificial intelligence.
Cui, Z., Lin, L., Zong, Y., Chen, Y., & Wang, S. Precision gene editing using deep learning: a case study of the crispr-cas9 editor.
Wang, B., He, Y., Shui, Z., Xin, Q., & Lei, H. (2024). Predictive optimization of DDoS attack mitigation in distributed systems using machine learning. Applied and Computational Engineering, 64, 95-100.
Xiao, J., Wang, J., Bao, W., Deng, T., & Bi, S. Application progress of natural language processing technology in financial research.
Wang, Yong, et al. (2024). Machine learning-based facial recognition for financial fraud prevention. Journal of Computer Technology and Applied Mathematics 1(1), 77-84.
Song, Jintong, et al. (2024). LSTM-based deep learning model for financial market stock price prediction. Journal of Economic Theory and Business Management, 1(2): 43-50.
Bai, Xinzhu, Wei Jiang, & Jiahao Xu. (2024). Development trends in AI-based financial risk monitoring technologies. Journal of Economic Theory and Business Management 1(2), 58-63.
Tian, J., Li, H., Qi, Y., Wang, X., & Feng, Y. (2024). Intelligent medical detection and diagnosis assisted by deep learning. Appl. Comput. Eng., 64, 116–121. https://doi.org/10.54254/2755-2721/64/20241356.
Shi, Y., Li, L., Li, H., Li, A., & Lin, Y. (2024). Aspect-level sentiment analysis of customer reviews based on neural multi-task learning. Journal of Theory and Practice of Engineering Science, 4(04), 1-8.
Li, Zihan, et al. (2024). Robot navigation and map construction based on SLAM technology.
Fan, C., Ding, W., Qian, K., Tan, H., & Li, Z. (2024). Cueing flight object trajectory and safety prediction based on SLAM technology. Journal of Theory and Practice of Engineering Science, 4(05), 1-8.
Ding, W., Tan, H., Zhou, H., Li, Z., & Fan, C. (2024). Immediate traffic flow monitoring and management based on multimodal data in cloud computing. Appl. Comput. Eng., 71, 1–6. https://doi.org/10.54254/2755-2721/71/2024ma0052.
Qian, K., Fan, C., Li, Z., Zhou, H., & Ding, W. (2024). Implementation of artificial intelligence in investment decision-making in the chinese a-share market. Journal of Economic Theory and Business Management, 1(2), 36-42.
Shi, Y., Yuan, J., Yang, P., Wang, Y., & Chen, Z. (2024). Implementing intelligent predictive models for patient disease risk in cloud data warehousing. Appl. Comput. Eng., 67, 34–40. https://doi.org/10.54254/2755-2721/67/2024ma0059.
Zhan, T., Shi, C., Shi, Y., Li, H., & Lin, Y. (2024). Optimization techniques for sentiment analysis based on LLM (GPT-3). Appl. Comput. Eng., 67, 41–47. https://doi.org/10.54254/2755-2721/67/2024ma0060.
Li, Huixiang, et al. (2024). AI face recognition and processing technology based on GPU computing. Journal of Theory and Practice of Engineering Science, 4(05), 9-16.
Bi, Shuochen, Wenqing Bao, Jue Xiao, Jiangshan Wang, & Tingting Deng. (2024). Application and practice of AI technology in quantitative investment. arXiv preprint arXiv:2404.18184(2024).
Yuan, J., Lin, Y., Shi, Y., Yang, T., & Li, A. (2024). Applications of artificial intelligence generative adversarial techniques in the financial sector. Academic Journal of Sociology and Management, 2(3), 59-66.
Yu, D., Xie, Y., An, W., Li, Z., & Yao, Y. (2023, December). Joint coordinate regression and association for multi-person pose estimation, A pure neural network approach. in Proceedings of the 5th ACM International Conference on Multimedia in Asia, pp. 1-8.
Lin, Y., Li, A., Li, H., Shi, Y., & Zhan, X. (2024). GPU-Optimized image processing and generation based on deep learning and computer vision. Journal of Artificial Intelligence General science (JAIGS), 5(1), 39-49.
Chen, Zhou, et al. (2024). Application of cloud-driven intelligent medical imaging analysis in disease detection. Journal of Theory and Practice of Engineering Science 4(05), 64-71.
Wang, B., Lei, H., Shui, Z., Chen, Z., & Yang, P. (2024). Current state of autonomous driving applications based on distributed perception and decision-making.
Zhan, X., Shi, C., Li, L., Xu, K., & Zheng, H. (2024). Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models. Applied and Computational Engineering, 71, 21-26.
Wu, B., Xu, J., Zhang, Y., Liu, B., Gong, Y., & Huang, J. (2024). Integration of computer networks and artificial neural networks for an AI-based network operator. Applied and Computational Engineering, 64, 115-120.
Liang, P., Song, B., Zhan, X., Chen, Z., & Yuan, J. (2024). Automating the training and deployment of models in MLOps by integrating systems with machine learning. Appl. Comput. Eng., 67, 1–7. Available at: https://doi.org/10.54254/2755-2721/67/20240690.
Li, A., Yang, T., Zhan, X., Shi, Y., & Li, H. (2024). Utilizing data science and AI for customer churn prediction in marketing. Journal of Theory and Practice of Engineering Science, 4(05), 72-79.
Wu, B., Gong, Y., Zheng, H., Zhang, Y., Huang, J., & Xu, J. (2024). Enterprise cloud resource optimization and management based on cloud operations. Applied and Computational Engineering, 67, 8-14.
Xu, J., Wu, B., Huang, J., Gong, Y., Zhang, Y., & Liu, B. (2024). Practical applications of advanced cloud services and generative AI systems in medical image analysis. Appl. Comput. Eng., 64, 83–88. https://doi.org/10.54254/2755-2721/64/20241361.
Zhang, Y., Liu, B., Gong, Y., Huang, J., Xu, J., & Wan, W. (2024). Application of machine learning optimization in cloud computing resource scheduling and management. Appl. Comput. Eng., 64, 17–22. https://doi.org/10.54254/2755-2721/64/20241359.
Huang, J., Zhang, Y., Xu, J., Wu, B., Liu, B., & Gong, Y. (2024). Implementation of seamless assistance with Google Assistant leveraging cloud computing. Appl. Comput. Eng., 64, 170–176. https://doi.org/10.54254/2755-2721/64/20241383.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Rahul Sharma, Kritika Sharma, Priya Patel
This work is licensed under a Creative Commons Attribution 4.0 International License.
