Safeguarding Federated Learning From Data Reconstruction Attacks via Gradient Dropout

Federated Learning (FL) enables collaborative model training across distributed participants without sharing raw data, offering a privacy-preserving paradigm. However, recent studies on gradient inversion attacks have demonstrated the vulnerability of FL to adversaries who can reconstruct sensitive local training data from shared gradients. To mitigate this threat, we propose Gradient Dropout, a novel defense mechanism that disrupts reconstruction attempts while preserving model utility. Specifically, Gradient Dropout perturbs gradients by randomly scaling a subset of components and replacing the remainder with Gaussian noise, thereby creating a transformed gradient space that significantly impedes reconstruction attempts. Moreover, this mechanism is applied across all layers of the model, ensuring that attackers cannot exploit any unperturbed gradients. Theoretical analysis reveals that the perturbed gradients can be kept sufficiently distant from their true values, thereby providing safety guarantees for the proposed algorithm. Furthermore, we demonstrate that this protection mechanism minimally impacts model performance, as gradient dropout and the original training dynamics remain effectively bounded under certain convexity conditions. These findings are substantiated through experimental evaluations, where we show that various attack methods yield low-quality reconstructed images while model performance is largely preserved, with less than 2% accuracy reduction relative to the baseline. As such, Gradient Dropout is presented as an effective solution for safeguarding privacy in FL, providing a balanced trade-off between privacy protection, computational efficiency, and model accuracy.