Federated Learning Approaches for Privacy-Preserving Artificial Intelligence in Distributed Cloud Environments
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P114Keywords:
Federated Learning, Privacy-Preserving AI, Cloud Computing, Secure Aggregation, Edge–Cloud CollaborationAbstract
Recent growth of artificial intelligence (AI) in cloud computing has accelerated the need to address data privacy, security and regulatory compliance because standard centralized training paradigm enforced to date necessitate sensitive business data to be centralized at a single point. These architectures also face organizations with the increased risks of data in leakage, unauthorized access, and inference attacks, especially in multi-tenant and geographically distributed cloud infrastructure. To overcome these issues, federated learning (FL) has become an encouraging paradigm of decentralized learning, which allows the training of models without leaving raw data out of the local sources. The paper explores federated learning solutions to privacy saving AI on distributed clouds with its focus on architectural design, privacy-enhancing techniques, and optimizations at the system level. We suggest a cloud-native federated learning system that combines secure aggregation systems, differentiation privacy systems and adaptive communication techniques to trade privacy, model precision and scale. Throughout an intensive examination, it is shown that the suggested strategy would greatly reduce the threat on privacy, and at the same time, offer contending learning results even in heterogeneous and resource-limited cloud environments. The main points of interest indicate that privacy-saving upgrades cause controllable information processing and communication costs in the case that they are well-coordinated within the framework of modern cloud solutions. The main contributions here are the analysis of the privacy risks of a cloud-based AI, the development of a federated learning framework that is specifically designed to be deployed in a distributed cloud, and practical considerations in the creation of scalable and privacy-regulated AI systems. The results highlight the possibility of federated learning, as the basis of trustful and privacy-conscious artificial intelligence in the next-generation cloud ecosystem.
References
[1] Liu, J., Huang, J., Zhou, Y., Li, X., Ji, S., Xiong, H., & Dou, D. (2022). From distributed machine learning to federated learning: A survey. Knowledge and information systems, 64(4), 885-917.
[2] Mothukuri, V., Parizi, R. M., 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. https://doi.org/10.1016/j.future.2020.10.007
[3] Kim, M., Lee, S., & Lee, D. (2022). Privacy-preserving federated learning using homomorphic encryption. Applied Sciences, 12(2), Article 734. https://doi.org/10.3390/app12020734
[4] Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775.
[5] So, J., Guler, B., & Avestimehr, A. (2020). Turbo-Aggregate: Breaking the quadratic aggregation barrier in secure federated learning. arXiv preprint arXiv:2002.04156.
[6] Baek, C., Kim, S., Nam, D., & Park, J. (2021). Enhancing differential privacy for federated learning at scale. IEEE Access, 9, 148090-148103.
[7] El Ouadrhiri, A., & Abdelhadi, A. (2022). Differential privacy for deep and federated learning: A survey. IEEE access, 10, 22359-22380.
[8] Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2021). Federated learning for healthcare informatics: privacy and data protection issues. IEEE Transactions on Industrial Informatics, 17(8), 5555–5565. doi:10.1109/TII.2021.3061543
[9] Bao, G., & Guo, P. (2022). Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges. Journal of Cloud Computing, 11(1), 94.
[10] Al-Quraan, M., Mohjazi, L., Bariah, L., Centeno, A., Zoha, A., Muhaidat, S., Debbah, M., & Imran, M. A. (2021). Edge-native intelligence for 6G communications driven by federated learning: A survey of trends and challenges. arXiv preprint arXiv:2111.07392.
[11] Pouriyeh, S., Shahid, O., Parizi, R. M., Sheng, Q. Z., Srivastava, G., Zhao, L., & Nasajpour, M. (2022). Secure smart communication efficiency in federated learning: Achievements and challenges. Applied Sciences, 12(18), 8980.
[12] Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305-311. Image 1
[13] Mo, F., Haddadi, H., Katevas, K., Marin, E., Perino, D., & Kourtellis, N. (2021, June). PPFL: Privacy-preserving federated learning with trusted execution environments. In Proceedings of the 19th annual international conference on mobile systems, applications, and services (pp. 94-108).
[14] Zhu, H., Zhang, H., & Jin, Y. (2021). From federated learning to federated neural architecture search: a survey. Complex & Intelligent Systems, 7(2), 639-657.
[15] Lo, S. K., Lu, Q., Zhu, L., Paik, H. Y., Xu, X., & Wang, C. (2022). Architectural patterns for the design of federated learning systems. Journal of Systems and Software, 191, 111357.
[16] Iacob, N. M., & Moise, M. L. (2015). Centralized vs. distributed databases. Case study. Academic Journal of Economic Studies, 1(4), 119-130.
[17] Zhang, J., Zhu, H., Wang, F., Zhao, J., Xu, Q., & Li, H. (2022). Security and privacy threats to federated learning: Issues, methods, and challenges. Security and Communication Networks, 2022(1), 2886795.
[18] Shen, S., Zhu, T., Wu, D., Wang, W., & Zhou, W. (2022). From distributed machine learning to federated learning: In the view of data privacy and security. Concurrency and Computation: Practice and Experience, 34(16), e6002.
[19] Zhao, J., Chen, Y., & Zhang, W. (2019). Differential privacy preservation in deep learning: Challenges, opportunities and solutions. IEEE Access, 7, 48901-48911.
[20] Kornaros, G. (2022). Hardware-assisted machine learning in resource-constrained IoT environments for security: review and future prospective. IEEE Access, 10, 58603-58622.
[21] Zhou, C., Fu, A., Yu, S., Yang, W., Wang, H., & Zhang, Y. (2020). Privacy-preserving federated learning in fog computing. IEEE Internet of Things Journal, 7(11), 10782-10793.
[22] Wahab, O. A., Mourad, A., Otrok, H., & Taleb, T. (2021). Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems. IEEE Communications Surveys & Tutorials, 23(2), 1342-1397.
[23] Christensen, J. (2021). AI in financial services. In Demystifying AI for the Enterprise (pp. 149-192). Productivity Press.
