Federated Learning(FL), in theory, preserves privacy of individual clients' data while producing quality machine learning models. However, attacks such as Deep Leakage from Gradients(DLG) severely question the practicality of FL. In this paper, we empirically evaluate the efficacy of four defensive methods against DLG: Masking, Clipping, Pruning, and Noising. Masking, while only previously studied as a way to compress information during parameter transfer, shows surprisingly robust defensive utility when compared to the other three established methods. Our experimentation is two-fold. We first evaluate the minimum hyperparameter threshold for each method across MNIST, CIFAR-10, and lfw datasets. Then, we train FL clients with each method and their minimum threshold values to investigate the trade-off between DLG defense and training performance. Results reveal that Masking and Clipping show near to none degradation in performance while obfuscating enough information to effectively defend against DLG.

本研究针对联邦学习中深层泄漏问题，评估了四种防御方法的有效性，包括掩蔽、裁剪、修剪和加噪声。研究表明，掩蔽作为防御手段，在保持训练性能的同时，能有效地抵御深层泄漏，展现出优于其他方法的防御能力。

随机梯度掩蔽作为针对联邦学习深层泄漏的防御措施