A Privacy-Preserving Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition - Zhang, Chen; Hu, Xiongwei; Xie, Yu; Gong, Maoguo; Yu, Bin (Frontiers in Neurorobotics, 2020)
Recently, multi-task learning (MTL) has been extensively studied for various face processing tasks including face detection, landmarks localization, pose estimation and gender recognition, which endeavors to train a better model by exploiting the synergy among the related tasks. However, the raw face dataset used for training often contains sensitive and private information, which can be maliciously recovered by carefully analyzing the model and outputs. To address this problem, we propose a novel privacy-preserving multi-task learning approach, which utilizes the differential private stochastic gradient descent algorithm to optimize the end-to-end multi-task model, and weighs loss functions of multiple tasks to improve learning efficiency and prediction accuracy. Specifically, calibrated noise is added to the gradient of loss functions to preserve privacy of the training data during model training. Furthermore, we exploit the homoscedastic uncertainty to balance different learning tasks. The experiments demonstrate that the proposed approach yields the differential privacy guarantees without decreasing accuracy of HyperFace under a desirable privacy budget.