Privacy-Preserving Federated Learning on AWS Using NVIDIA FLARE: Advances in Secure and Distributed AI Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P102Keywords:
Federated Learning, Privacy-Preserving, Differential Privacy, Secure Multi-Party Computation, Homomorphic Encryption, AWS, NVIDIA FLARE, Machine Learning, Data Security, Cloud ComputingAbstract
Federated Learning (FL) is an emerging paradigm in machine learning that enables multiple parties to collaboratively train models without sharing their data. This approach addresses critical privacy and data security concerns, making it particularly suitable for sensitive domains such as healthcare, finance, and personal data management. This paper explores the implementation of Privacy-Preserving Federated Learning (PPFL) on Amazon Web Services (AWS) using NVIDIA FLARE, a framework designed to facilitate the development and deployment of FL applications. We delve into the technical details of PPFL, the integration of NVIDIA FLARE with AWS, and the security mechanisms employed to ensure data privacy. We also present a case study and experimental results to demonstrate the effectiveness and efficiency of the proposed system. The paper concludes with a discussion on the future directions and potential challenges in the field of PPFL
References
[1] McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics (pp. 1273-1282).
[2] Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., ... & Sethi, R. (2017). Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1175-1191).
[3] Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), 211-407.
[4] Gentry, C. (2009). A fully homomorphic encryption scheme. Stanford University.
[5] Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP) (pp. 3-18).
[6] NVIDIA. (2021). NVIDIA FLARE: Federated Learning and Analytics at the Edge. [Online]. Available: https://developer.nvidia.com/nvidia-flare
[7] Amazon Web Services. (2021). Amazon Web Services. [Online]. Available: https://aws.amazon.com
[8] Amazon Web Services. (2021, August 23). Privacy-preserving federated learning on AWS with NVIDIA FLARE. AWS Partner Network (APN) Blog. https://aws.amazon.com/blogs/apn/privacy-preserving-federated-learning-on-aws-with-nvidia-flare/
[9] NVIDIA Developer. (n.d.). NVIDIA FLARE. https://developer.nvidia.com/flare
[10] AWS Partner Network. (2021, August 23). Federated learning on AWS with NVIDIA. https://aws.amazon.com/de/awstv/watch/d2ddd544829/
[11] NVIDIA Developer. (2022, October 24). Turning machine learning to federated learning in minutes with NVIDIA FLARE 2.4. https://developer.nvidia.com/blog/turning-machine-learning-to-federated-learning-in-minutes-with-nvidia-flare-2-4/
[12] AWS Machine Learning Blog. (2022, June 15). Reinventing a cloud-native federated learning architecture on AWS. https://aws.amazon.com/blogs/machine-learning/reinventing-a-cloud-native-federated-learning-architecture-on-aws/
[13] NVIDIA GitHub. (n.d.). NVIDIA FLARE documentation. https://nvidia.github.io/NVFlare
[14] Roth, H. R., Cheng, Y., Wen, Y., Yang, I., Xu, Z., Hsieh, Y.-T., Kersten, K., Harouni, A., Zhao, C., Lu, K., Zhang, Z., Li, W., Myronenko, A., Yang, D., Yang, S., Rieke, N., Quraini, A., Chen, C., Xu, D., Ma, N., Dogra, P., Flores, M., & Feng, A. (2022). NVIDIA FLARE: Federated learning from simulation to real-world. arXiv preprint arXiv:2210.13291. https://arxiv.org/abs/2210.13291
[15] NVIDIA FLARE Documentation. (n.d.). NVIDIA FLARE overview. https://nvflare.readthedocs.io/en/2.5/flare_overview.html
[16] AWS Partner Network. (n.d.). NVIDIA. https://aws.amazon.com/blogs/apn/tag/nvidia/
[17] Roth, H. R., Cheng, Y., Wen, Y., Yang, I., Xu, Z., Hsieh, Y.-T., Kersten, K., Harouni, A., Zhao, C., Lu, K., Zhang, Z., Li, W., Myronenko, A., Yang, D., Yang, S., Rieke, N., Quraini, A., Chen, C., Xu, D., Ma, N., Dogra, P., Flores, M., & Feng, A. (2022). NVIDIA FLARE: Federated learning from simulation to real-world. arXiv preprint arXiv:2210.13291. https://arxiv.org/abs/2210.13291
[18] Amazon Web Services. (2021, August 23). Privacy-preserving federated learning on AWS with NVIDIA FLARE. AWS Partner Network (APN) Blog. https://aws.amazon.com/blogs/apn/privacy-preserving-federated-learning-on-aws-with-nvidia-flare/
[19] NVIDIA Developer. (n.d.). NVIDIA FLARE. https://developer.nvidia.com/flare
[20] AWS Partner Network. (2021, August 23). Federated learning on AWS with NVIDIA. https://aws.amazon.com/de/awstv/watch/d2ddd544829/
[21] NVIDIA Developer. (2022, October 24). Turning machine learning to federated learning in minutes with NVIDIA FLARE 2.4. https://developer.nvidia.com/blog/turning-machine-learning-to-federated-learning-in-minutes-with-nvidia-flare-2-4/
[22] AWS Machine Learning Blog. (2022, June 15). Reinventing a cloud-native federated learning architecture on AWS. https://aws.amazon.com/blogs/machine-learning/reinventing-a-cloud-native-federated-learning-architecture-on-aws/
[23] NVIDIA GitHub. (n.d.). NVIDIA FLARE documentation. https://nvidia.github.io/NVFlare
[24] Roth, H. R., Cheng, Y., Wen, Y., Yang, I., Xu, Z., Hsieh, Y.-T., Kersten, K., Harouni, A., Zhao, C., Lu, K., Zhang, Z., Li, W., Myronenko, A., Yang, D., Yang, S., Rieke, N., Quraini, A., Chen, C., Xu, D., Ma, N., Dogra, P., Flores, M., & Feng, A. (2022). NVIDIA FLARE: Federated learning from simulation to real-world. arXiv preprint arXiv:2210.13291. https://arxiv.org/abs/2210.13291
[25] Amazon Web Services. (2021, August 23). Privacy-preserving federated learning on AWS with NVIDIA FLARE. AWS Partner Network (APN) Blog. https://aws.amazon.com/blogs/apn/privacy-preserving-federated-learning-on-aws-with-nvidia-flare/
