Assessing the Feasibility of Federated Learning Deployment in Multi-Cloud AI Ecosystems
Keywords:
Federated Learning, Multi-cloud AI, Data Sovereignty, Edge AI,, Cloud Interop-erability, Privacy-preserving MLAbstract
The rise of data sovereignty concerns and privacy regulations has prompted a shift toward decentralized machine learning models. Federated Learning (FL), with its promise of data locality and collaborative model training, is a pivotal innovation. This paper examines the feasibility of deploying FL in multi-cloud AI ecosystems, focusing on infrastructure heterogeneity, data residency policies, interoperability, and performance benchmarks. We assess support across major cloud providers and model types, identifying technical barriers and strategic enablers for successful FL adoption.
References
Kairouz, Peter, et al. "Advances and open problems in federated learning." arXiv preprint arXiv:1912.04977, 2019.
Subramanyam, S.V. (2019). The role of artificial intelligence in revolutionizing healthcare business process automation. International Journal of Computer Engi-neering and Technology (IJCET), 10(4), 88–103.
Bonawitz, Keith, et al. "Towards federated learning at scale: System design." Pro-ceedings of Machine Learning and Systems, 2020.
Zhang, Cheng, et al. "Secure federated learning for multi-cloud systems." IEEE Transactions on Cloud Computing, vol. 9, no. 4, 2021, pp. 1256–1270.
Subramanyam, S.V. (2022). AI-powered process automation: Unlocking cost effi-ciency and operational excellence in healthcare systems. International Journal of Advanced Research in Engineering and Technology (IJARET), 13(1), 86–102.
Rahman, Md Abdur, et al. "Federated learning with homomorphic encryption in multi-cloud environments." Future Generation Computer Systems, vol. 125, 2022, pp. 250–264.
Li, Tian, et al. "Federated learning for healthcare informatics." IEEE Intelligent Sys-tems, vol. 35, no. 2, 2020, pp. 20–28.
Subramanyam, S.V. (2023). The intersection of cloud, AI, and IoT: A pre-2021 framework for healthcare business process transformation. International Journal of Cloud Computing (IJCC), 1(1), 53–69.
Sattler, Felix, et al. "Adaptive federated learning in resource-constrained edge com-puting." IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, 2021, pp. 142–156.
Truong, Hien, et al. "Orchestration of federated learning workflows in cloud-edge environments." Proceedings of the 22nd ACM/IFIP International Middleware Con-ference, 2021, pp. 121–134.
Lyu, Lingjuan, Huan Yu, and Qiang Yang. "Threats to federated learning: A survey." IEEE Transactions on Big Data, vol. 7, no. 3, 2021, pp. 627–648.
Zhu, Heng, et al. "A survey on privacy-preserving federated learning: Current solu-tions and future directions." IEEE Internet of Things Journal, vol. 8, no. 6, 2021, pp. 4481–4494.
Mohassel, Payman, and Yupeng Zhang. "SecureML: A system for scalable priva-cy-preserving machine learning." 2017 IEEE Symposium on Security and Privacy (SP), 2017, pp. 19–38.
Subramanyam, S.V. (2021). Cloud computing and business process re-engineering in financial systems: The future of digital transformation. International Journal of In-formation Technology and Management Information Systems (IJITMIS), 12(1), 126–143.
McMahan, Brendan, et al. "Learning differentially private recurrent language mod-els." International Conference on Learning Representations (ICLR), 2018.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Edward Johnson (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
