Evaluating Federated Learning Scenarios in a Tumor Classification Application
Resumo
Federated Learning is a new area of distributed Machine Learning (ML) that emerged to deal with data privacy concerns. In this approach, each client has access to a local and private dataset. They only exchange the model weights and updates. This paper presents a Federated Learning (FL) approach to a cloud Tumor-Infiltrating Lymphocytes (TIL) application. The results show that the FL approach outperformed the centralized one in all evaluated ML metrics. It also reduced the execution time although the financial cost has increased.
Referências
Abadi, M. et al. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org.
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., and Lane, N. (2020). Flower: A friendly federated learning research framework. ArXiv, abs/2007.14390.
ElGamal, T. (1985). A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Transactions on Information Theory, 31(4):469–472.
Fang, C., Guo, Y., Wang, N., and Ju, A. (2020). Highly efficient federated learning with strong privacy preservation in cloud computing. Computers & Security, 96:101889.
Liu, L., Zhang, J., Song, S., and Letaief, K. B. (2020). Client-edge-cloud hierarchical federated learning. In 2020-2020 IEEE International Conference on Communications.
McMahan, B. et al. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proc. of 20th Int. Conf. on Artificial Intelligence and Statistics.
Paszke, A. et al. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems.
Rajendran, S. et al. (2021). Cloud-based federated learning implementation across medical centers. JCO Clinical Cancer Informatics.
Ryffel, T. et al. (2018). A generic framework for privacy preserving deep learning. ArXiv, abs/1811.04017.
Saltz, J. et al. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell reports.
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In 3rd Int. Conf. on Learning Representations, ICLR 2015.
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., and Lane, N. (2020). Flower: A friendly federated learning research framework. ArXiv, abs/2007.14390.
ElGamal, T. (1985). A public key cryptosystem and a signature scheme based on discrete logarithms. IEEE Transactions on Information Theory, 31(4):469–472.
Fang, C., Guo, Y., Wang, N., and Ju, A. (2020). Highly efficient federated learning with strong privacy preservation in cloud computing. Computers & Security, 96:101889.
Liu, L., Zhang, J., Song, S., and Letaief, K. B. (2020). Client-edge-cloud hierarchical federated learning. In 2020-2020 IEEE International Conference on Communications.
McMahan, B. et al. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proc. of 20th Int. Conf. on Artificial Intelligence and Statistics.
Paszke, A. et al. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems.
Rajendran, S. et al. (2021). Cloud-based federated learning implementation across medical centers. JCO Clinical Cancer Informatics.
Ryffel, T. et al. (2018). A generic framework for privacy preserving deep learning. ArXiv, abs/1811.04017.
Saltz, J. et al. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell reports.
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In 3rd Int. Conf. on Learning Representations, ICLR 2015.
Publicado
29/11/2021
Como Citar
BRUM, Rafaela C.; TEODORO, George; DRUMMOND, Lúcia; ARANTES, Luciana ; CASTRO, Maria Clicia; SENS, Pierre.
Evaluating Federated Learning Scenarios in a Tumor Classification Application. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DO RIO DE JANEIRO (ERAD-RJ), 7. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 6-10.
DOI: https://doi.org/10.5753/eradrj.2021.18558.
