Dropout against Deep Leakage from Gradients
This addresses a critical privacy issue in federated learning for machine learning practitioners, but it is incremental as it builds on existing dropout techniques.
The paper tackles the problem of deep leakage from gradients in federated learning, where raw data can be recovered from shared gradients, and proposes using an additional dropout layer to prevent this, achieving that training data cannot converge to a small RMSE even after 5,800 epochs with a dropout rate of 0.5.
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.