  As the scale and size of the data increases significantly nowadays, federal
learning (Bonawitz et al. [2019]) for high performance computing and machine
learning has been much more important than ever before (Abadi et al. [2016]).
People used to believe that sharing gradients seems to be safe to conceal the
local training data during the training stage. However, Zhu et al. [2019]
demonstrated that it was possible to recover raw data from the model training
data by detecting gradients. They use generated random dummy data and minimise
the distance between them and real data. Zhao et al. [2020] pushes the
convergence algorithm even further. By replacing the original loss function
with cross entropy loss, they achieve better fidelity threshold. In this paper,
we propose using an additional dropout (Srivastava et al. [2014]) layer before
feeding the data to the classifier. It is very effective in preventing leakage
of raw data, as the training data cannot converge to a small RMSE even after
5,800 epochs with dropout rate set to 0.5.

Machine Learning;Cryptography and Security