Federated Learning in Cloud-Native Architectures: A Secure Approach to Decentralized AI
DOI:
https://doi.org/10.47941/ijce.2762Keywords:
Federated Learning (FL), Cloud-Native Architectures, Decentralized AI, Model Inversion Attacks, AI Security.Abstract
Purpose: The paper aims to analyze the technical and security challenges of deploying FL at scale and explores how modern cloud-native technologies such as container orchestration, hybrid cloud infrastructure, and privacy-preserving techniques can be leveraged to mitigate these challenges. The study also seeks to provide a comprehensive understanding of how FL is being applied in critical domains such as healthcare, IoT, and cybersecurity, while identifying future trends that could shape the evolution of decentralized AI systems.
Methodology: This research adopts a qualitative and architectural analysis approach to evaluate the intersection of Federated Learning and cloud-native computing. A systematic review of the current state-of-the-art technologies supporting FL, including Docker containers, Kubernetes orchestration, and hybrid cloud environments. A threat modeling analysis focusing on prevalent security risks such as data poisoning, model inversion, and Byzantine node attacks. An evaluation of security frameworks and privacy-enhancing technologies (e.g., differential privacy, secure multi-party computation, and homomorphic encryption) used to protect FL systems.
Findings: The study finds that cloud-native architectures provide a robust and flexible foundation for scaling Federated Learning systems. Kubernetes-based orchestration and containerization significantly enhance the deployment and scalability of FL models across heterogeneous environments.
Unique Contribution to Theory, Practice and Policy: While FL minimizes raw data exchange, it introduces unique attack vectors; effective mitigation requires multi-layered security, including encryption protocols and node validation mechanisms. Techniques such as differential privacy and homomorphic encryption provide meaningful protections but must be carefully balanced against performance overhead.
Downloads
References
B. McMahan et al., "Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273-1282.
T. Li, A. Sahu, A. Talwalkar, and V. Smith, "Federated Learning: Challenges, Methods, and Future Directions," IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50-60, 2020.
K. Bonawitz et al., "Towards Federated Learning at Scale: System Design," in Proceedings of the 2nd MLSys Conference, 2020.
Y. Liu, K. Kang, and J. Wang, "A Cloud-Native Federated Learning Framework for Edge Computing," IEEE Transactions on Cloud Computing, vol. 10, no. 2, pp. 315-329, 2022.
P. Kairouz et al., "Advances and Open Problems in Federated Learning," Foundations and Trends in Machine Learning, vol. 14, no. 1–2, pp. 1-210, 2021.
R. Shokri and V. Shmatikov, "Privacy-Preserving Deep Learning," in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS), 2015, pp. 1310-1321.
B. McMahan et al., "Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273-1282.
T. Li, A. Sahu, A. Talwalkar, and V. Smith, "Federated Learning: Challenges, Methods, and Future Directions," IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50-60, 2020.
Y. Liu, K. Kang, and J. Wang, "A Cloud-Native Federated Learning Framework for Edge Computing," IEEE Transactions on Cloud Computing, vol. 10, no. 2, pp. 315-329, 2022.
R. Shokri and V. Shmatikov, "Privacy-Preserving Deep Learning," in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS), 2015, pp. 1310-1321.
K. Bonawitz et al., "Towards Federated Learning at Scale: System Design," in Proceedings of the 2nd MLSys Conference, 2020.
S. Wang, T. Tuor, and S. Velipasalar, "Federated Learning at the Edge: A Multi-Agent Approach," IEEE Internet of Things Journal, vol. 8, no. 4, pp. 2856-2871, 2021.
C. Ma, Y. Kong, and Q. Zhang, "Hybrid Cloud Approaches for Privacy-Preserving Federated Learning," IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 3, pp. 1112-1125, 2022.
P. Kairouz et al., "Advances and Open Problems in Federated Learning," Foundations and Trends in Machine Learning, vol. 14, no. 1–2, pp. 1-210, 2021.
M. Zhang, J. Lin, and W. Xu, "Blockchain-Based Federated Learning: A Secure AI Model Training Approach," IEEE Transactions on Blockchain, vol. 3, no. 2, pp. 85-97, 2020.
J. Liu, A. G. Parada, and H. Chen, "Kubeflow and TensorFlow Federated for Secure AI Training," IEEE Transactions on Cloud Computing, vol. 9, no. 1, pp. 58-70, 2023.
B. McMahan et al., "Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273-1282.
P. Kairouz et al., "Advances and Open Problems in Federated Learning," Foundations and Trends in Machine Learning, vol. 14, no. 1–2, pp. 1-210, 2021.
C. Dwork, "Differential Privacy," in Proceedings of the 33rd International Colloquium on Automata, Languages and Programming (ICALP), 2006, pp. 1-12.
Y. Bonawitz et al., "Practical Secure Aggregation for Privacy-Preserving Machine Learning," in Proceedings of the 2017 ACM Conference on Computer and Communications Security (CCS), pp. 1175-1191, 2017.
Z. Brakerski, C. Gentry, and V. Vaikuntanathan, "Fully Homomorphic Encryption Without Bootstrapping," in Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (ITCS), 2012, pp. 309-325.
X. Zhang, S. Chen, and J. Zhao, "Federated Learning with Knowledge Distillation for Efficient Communication and Privacy," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 4, pp. 1718-1731, 2021.
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, "How to Backdoor Federated Learning," in Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020, pp. 2938-2948.
G. Melis, C. Song, V. Shmatikov, and M. Zanella-Béguelin, "Exploiting Unintended Feature Leakage in Collaborative Learning," in Proceedings of the 35th International Conference on Machine Learning (ICML), 2019, pp. 6716-6725.
L. Blanchard, R. Guerraoui, and J. Stainer, "Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent," in Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS), 2017, pp. 1-11.
R. Shokri et al., "Membership Inference Attacks Against Machine Learning Models," in Proceedings of the 2017 IEEE Symposium on Security and Privacy (S&P), 2017, pp. 3-18.
N. Hynes, R. Cheng, and D. Song, "Efficient Deep Learning on Multi-Source Private Data," in Proceedings of the 2018 International Conference on Learning Representations (ICLR), 2018, pp. 1-13.
M. Zhang, J. Lin, and W. Xu, "Blockchain-Based Federated Learning: A Secure AI Model Training Approach," IEEE Transactions on Blockchain, vol. 3, no. 2, pp. 85-97, 2020.
T. Doshi, M. Jaiswal, and A. Anand, "Interpretable Machine Learning for Trustworthy Federated Learning Models," in Proceedings of the 2021 IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 123-130.
P. Kairouz et al., "Advances and Open Problems in Federated Learning," Foundations and Trends in Machine Learning, vol. 14, no. 1–2, pp. 1-210, 2021.
R. Sheller et al., "Federated Learning in Medicine: Facilitating Multi-Institutional Collaborations Without Sharing Patient Data," Scientific Reports, vol. 10, no. 1, pp. 1-12, 2020.
B. Kaissis et al., "End-to-End Privacy Preserving Deep Learning on Multi-Institutional Medical Imaging," Nature Machine Intelligence, vol. 2, no. 6, pp. 305-311, 2020.
A. Rieke et al., "The Future of Digital Health with Federated Learning," npj Digital Medicine, vol. 3, no. 1, pp. 1-7, 202A. Rieke et al., "The Future of Digital Health with Federated Learning," npj Digital Medicine, vol. 3, no. 1, pp. 1-7, 2020.
M. S. H. Abad et al., "Hierarchical Federated Learning for Edge Computing: A Scalable and Privacy-Preserving Approach," IEEE Transactions on Mobile Computing, vol. 20, no. 10, pp. 3065-3079, 2021.
K. Bonawitz et al., "Towards Federated Learning at Scale: System Design," in Proceedings of the 2nd MLSys Conference, 2020.
H. Wang et al., "Federated Learning for Personalized Healthcare AI," IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3174-3185, 2021.
C. Ma, Y. Kong, and Q. Zhang, "Hybrid Cloud Approaches for Privacy-Preserving Federated Learning," IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 3, pp. 1112-1125, 2022.
Z. Yang, X. Liu, and S. Chen, "Federated Learning-Based Fraud Detection in Financial Transactions," IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 7, pp. 3125-3136, 2022.
A. Hardy et al., "Privacy-Preserving Credit Scoring Using Federated Learning," Proceedings of the 2021 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 45-54, 2021.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Pramod Ganore

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.