1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X., 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. URL: https://www.tensorflow.org/. software available from tensorflow.org.
2. Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L., Deep learning with differential privacy, in: The 2016 ACM CCS.
3. Agarap, A.F., 2018. Deep learning using rectified linear units (relu). CoRR abs/1803.08375. url:http://arxiv.org/abs/1803.08375, arXiv:1803.08375.
4. Amiri, M.M., Gunduz, D., Kulkarni, S.R., Poor, H.V., 2020. Federated learning with quantized global model updates. arXiv preprint arXiv:2006.10672.
5. Recommendation for key management – part 1: General (revision 3);Barker;NIST Special Publication Revision 3,2005