Flautim: A Federated Learning Platform using K8S and Flower
Resumo
Federated learning (FL) is a cutting-edge technology in artificial intelligence that preserves data privacy and security while reducing the cost of computation and communication. It transforms traditional centralized machine learning and deep learning approaches to enable decentralized model training without the need for data exchange. This work presents Flautim, the first implementation of an FL platform in Brazil and all around Latin America based on Kubernetes (K8S) and Flower framework. Flautim is designed for academic use, enabling researchers without a technical background to conduct FL experiments on this platform easily. Also, this platform allows for the development of applications involving data gathered from connected vehicles. Thus, this study aims to introduce this new FL platform, providing comprehensive details of its architecture.
Palavras-chave:
Artificial intelligence, machine learning, deep learning, federated learning, data privacy and security
Referências
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L. A. Vayghan, M. A. Saied, M. Toeroe, and F. Khendek, “Deploying microservice based applications with kubernetes: Experiments and lessons learned,” in 2018 IEEE 11th international conference on cloud computing (CLOUD). IEEE, 2018, pp. 970–973.
A. Rahman, R. Mahdavi-Hezaveh, and L. Williams, “A systematic mapping study of infrastructure as code research,” Information and Software Technology, vol. 108 , pp. 65–77, 2019.
J. Sáinz-Pardo Díaz, A. Heredia Canales, I. Heredia Cachá, V. Tran, G. Nguyen, K. Alibabaei, M. Obregón Ruiz, S. Rebolledo Ruiz, and A. López García, “Making federated learning accessible to scientists: The ai4eosc approach,” ser. IH&MMSec ’24. New York, NY, USA: Association for Computing Machinery, 2024.
J. Konecný, H. McMahan, F. Yu, P. Richtárik, A. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” CoRR, vol. abs/1610.05492, 2016.
B. S. Guendouzi, S. Ouchani, H. E. Assaad, and M. E. Zaher, “A systematic review of federated learning: Challenges, aggregation methods, and development tools,” Journal of Network and Computer Applications, p. 103714, 2023.
M. Galtier and C. Marini, “Substra: a framework for privacy-preserving, traceable and collaborative machine learning,” 10 2019.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019.
A. Ziller, A. Trask, A. Lopardo, B. Szymkow, B. Wagner, E. Bluemke, J.-M. Nounahon, J. Passerat-Palmbach, K. Prakash, N. Rose, T. Ryffel, Z. N. Reza, and G. Kaissis, PySyft: A Library for Easy Federated Learning, 06 2021, pp. 111–139.
D. J. Beutel, T. Topal, A. Mathur, X. Qiu, J. Fernandez-Marques, Y. Gao, L. Sani, K. H. Li, T. Parcollet, P. P. B. de Gusmão et al., “Flower: A friendly federated learning research framework,” arXiv preprint arXiv:2007.14390, 2020.
G. Reina, A. Gruzdev, P. Foley, O. Perepelkina, M. Sharma, I. Davidyuk, I. Trushkin, M. Radionov, A. Mokrov, D. Agapov, B. Edwards, M. Sheller, S. Pati, P. Moorthy, S.-h. Wang, P. Shah, and S. Bakas, “Openfl: An open-source framework for federated learning,” 05 2021.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics. PMLR, 2017, pp. 1273–1282.
H. Ludwig, N. Baracaldo, G. Thomas, Y. Zhou, A. Anwar, S. Rajamoni, Y. Ong, J. Radhakrishnan, M. Sinn, M. Purcell, A. Rawat, T. Minh, N. Holohan, S. Chakraborty, S. Whitherspoon, D. Steuer, L. Wynter, H. Hassan, and A. Abay, “Ibm federated learning: an enterprise framework white paper v0.1,” 07 2020.
C. He, S. Li, J. So, X. Zeng, M. Zhang, H. Wang, X. Wang, P. Vepakomma, A. Singh, H. Qiu et al., “Fedml: A research library and benchmark for federated machine learning,” arXiv preprint arXiv:2007.13518, 2020.
J. Watada, A. Roy, R. Kadikar, H. Pham, and B. Xu, “Emerging trends, techniques and open issues of containerization: A review,” IEEE Access, vol. 7, pp. 152 443 – 152 472, 2019.
O. Bentaleb, A. S. Belloum, A. Sebaa, and A. El-Maouhab, “Containerization technologies: Taxonomies, applications and challenges,” The Journal of Supercomputing, vol. 78, no. 1, pp. 1144–1181, 2022.
A. M. Beltre, P. Saha, M. Govindaraju, A. Younge, and R. E. Grant, “Enabling hpc workloads on cloud infrastructure using kubernetes container orchestration mechanisms,” in 2019 IEEE/ACM International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC). IEEE, 2019, pp. 11–20.
S. Hardikar, P. Ahirwar, and S. Rajan, “Containerization: cloud computing based inspiration technology for adoption through docker and kubernetes,” in 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2021, pp. 1996–2003.
L. A. Vayghan, M. A. Saied, M. Toeroe, and F. Khendek, “Deploying microservice based applications with kubernetes: Experiments and lessons learned,” in 2018 IEEE 11th international conference on cloud computing (CLOUD). IEEE, 2018, pp. 970–973.
A. Rahman, R. Mahdavi-Hezaveh, and L. Williams, “A systematic mapping study of infrastructure as code research,” Information and Software Technology, vol. 108 , pp. 65–77, 2019.
Publicado
14/10/2024
Como Citar
BARROS, Pedro H. S. S. et al.
Flautim: A Federated Learning Platform using K8S and Flower. In: WORKSHOP DE FERRAMENTAS E APLICAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 30. , 2024, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 87-90.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2024.244009.
