Federated Learning in Cloud Environments: A Secure and Scalable AI Framework for Decentralized Data Processing
Keywords:
Federated Learning, Cloud Computing, Decentralized AI, Privacy Preservation, Edge Computing, Secure Aggregation, Distributed SystemsAbstract
The exponential growth of data generated by edge devices has made centralized AI training models increasingly inefficient and privacy-invasive. Federated Learning (FL), a decentralized learning paradigm, addresses these concerns by enabling model training across distributed clients without centralizing data. This paper explores a secure and scalable framework for implementing Federated Learning within cloud environments, focusing on system architecture, privacy preservation, and communication efficiency. We integrate previous scholarly efforts to develop a robust cloud-based FL framework and demonstrate its efficacy via architectural analysis and performance metrics.
References
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., ... & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1175–1191.
Rapolu, N.K. (2023). Building Resilient SAP Architectures for High Availability & Disaster Recovery Process on Onpremise & Azure Environment. International Journal of Innovative Research and Creative Technology, 9(6), 1–4. https://doi.org/10.5281/zenodo.14951538
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977.
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2019). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
Rapolu, N.K. (2023). Migration of legacy database Oracle/MS SQL to HANA database to improve real-time Online Analytical Processing and Online Transaction Processing from one data model. International Scientific Journal of Engineering and Management, 2(3), 1–4. https://doi.org/10.55041/ISJEM00215
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54, 1273–1282.
Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 1310–1321.
Rapolu, N.K. (2023). Configuring Single Sign-On Integration between BTP Applications and Google IDP, Azure AD, AWS & Okta to enable secure and seamless User Access. International Scientific Journal of Engineering and Management, 2(7), 1–4. https://doi.org/10.55041/ISJEM01288
Smith, V., Chiang, C. K., Sanjabi, M., & Talwalkar, A. (2017). Federated multi-task learning. Advances in Neural Information Processing Systems, 30.
Hard, A., Rao, K., Mathews, R., Beaufays, F., Augenstein, S., Eichner, H., ... & Ramage, D. (2018). Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604.
Rapolu, N.K. (2023). Integrating SAP ERP with Azure and AWS for Improved Operational Efficiency. Journal of Artificial Intelligence, Machine Learning, and Data Science, 1(1), 2395–2397. https://doi.org/10.51219/JAIMLD/naresh-kumar-rapolu/517
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–318.
Mohassel, P., & Zhang, Y. (2017). SecureML: A system for scalable privacy-preserving machine learning. IEEE Symposium on Security and Privacy, 19–38.
Rapolu, N.K. (2023). Advanced SAP Identity and Access Management with Integration to Azure AD and Other Identity Providers. International Journal of Leading Research Publication (IJLRP), 4(5), 1–6. https://doi.org/10.5281/zenodo.14960405
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018). Federated learning with non-IID data. arXiv preprint arXiv:1806.00582.
