Federated Learning for Privacy-Preserving AI in Hybrid Clouds: A Technical Overview
DOI:
https://doi.org/10.32628/CSEIT25111294Keywords:
Federated Learning, Hybrid Cloud Computing, Machine Learning, Privacy Preservation, Secure Data ProcessingAbstract
Federated Learning (FL) represents a transformative approach to machine learning in hybrid cloud environments, addressing critical challenges in data privacy and security while enabling collaborative model training across distributed environments. This comprehensive technical overview explores the architecture, implementation, and benefits of FL in hybrid cloud deployments. The article explores how organizations can leverage FL to maintain data sovereignty while achieving comparable or superior model performance to traditional centralized approaches. By analyzing various aspects including communication optimization, model initialization, local training, and global aggregation, this article demonstrates FL's effectiveness in preserving privacy while enabling cross-organizational collaboration. The article encompasses privacy preservation mechanisms, security protocols, and scalability considerations, providing insights into both current capabilities and future directions for enterprise implementations.
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Priya Ranjan Parida, et al., "Enterprise Architecture Frameworks for Cloud Transformation: Aligning Business Strategy with Cloud Migration Goals," Journal of Artificial Intelligence Research and Applications, 2024. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/299/288
Katharine Daly, et al., "Federated Learning in Practice: Reflections and Projections," arXiv preprint arXiv:2410.08892, Oct. 2023. Available: https://arxiv.org/html/2410.08892v1
Kunal Chandiramani, et al., "Performance Analysis of Distributed and Federated Learning Models on Private Data," Nternational Conference On Recent Trends In Advanced Computing 2019, Icrtac 2019. Available: https://www.researchgate.net/publication/339541559_Performance_Analysis_of_Distributed_and_Federated_Learning_Models_on_Private_Data
Lavanya Shanmugam, et al., "Federated Learning Architecture: Design, Implementation, and Challenges in Distributed AI Systems," Journal of Knowledge Learning and Science Technology, 2023. Available: https://jklst.org/index.php/home/article/view/172
Zhenyi Lin, et al., "Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis," arXiv preprint arXiv:2103.06056, March 2021. Available: https://arxiv.org/pdf/2103.06056
Osama Shahid, et al., "Communication Efficiency in Federated Learning: Achievements and Challenges," arXiv preprint arXiv:2107.10996, July 2021. Available: https://arxiv.org/pdf/2107.10996
Krishna Pillutla, et al., "Robust Aggregation for Federated Learning," IEEE Transactions on Signal Processing ( Volume: 70, 24 February 2022). Available: https://ieeexplore.ieee.org/abstract/document/9721118
Ali Beikmohammadi, et al., "On the Convergence of Federated Learning Algorithms without Data Similarity," arXiv preprint arXiv:2403.02347, March 2024. Available: https://arxiv.org/html/2403.02347v1
Nikolas Koutsoubis, et al., "Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review," arXiv preprint arXiv:2409.16340, Sept. 2024. Available: https://arxiv.org/pdf/2409.16340
Anbu Huang, et al., "StarFL: Hybrid Federated Learning Architecture for Smart Urban Computing," ACM Transactions on Intelligent Systems and Technology (TIST), Volume 12, Issue 4, 2021. Available: https://dl.acm.org/doi/abs/10.1145/3467956
Bassel Soudan, et al., "Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis," International Journal of Distributed Systems, vol. 12, no. 4, pp. 189-206, 2024. Available: https://www.researchgate.net/publication/378116038_Scalability_and_Performance_Evaluation_of_Federated_Learning_Frameworks_A_Comparative_Analysis
Mingzhe Chen, et al., "Communication-efficient federated learning," Proceedings of the National Academy of Sciences, vol. 118, no. 21, 2021. Available: https://www.pnas.org/doi/epub/10.1073/pnas.2024789118
Marc Haller, et al., "Handling Non-IID Data in Federated Learning: An Experimental Evaluation Towards Unified Metrics," IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) 2023. Available: https://ieeexplore.ieee.org/abstract/document/10361408
Betul Yurdem, et al., "Federated learning: Overview, strategies, applications, tools and future directions," Heliyon, vol. 10, no. 3, Article e24141, March 2024. Available: https://www.sciencedirect.com/science/article/pii/S2405844024141680
Shaoxiong Ji, et al., "Emerging trends in federated learning: from model fusion to federated X learning," International Journal of Machine Learning and Cybernetics, vol. 15, pp. 1-23, Jan. 2024. Available: https://link.springer.com/article/10.1007/s13042-024-02119-1
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