1.Inverting Gradients - How easy is it to break privacy in federated learning? 【CVPR22】 Attack goal:在真实架构联邦学习场景下,考虑现实模型深度,进行多图像分类任务,利用图像的梯度参数重建出高分辨率的图像。 左图是原始数据,中间图像是从在ImageNet上训练过的ResNet-18进行重建的,右图是从训练过的ResNet...
Inverting Gradients - How easy is it to break Privacy in Federated Learning? Update Feb 2022: A modernized implementation of this attack (and many other attacks) is included in our newest framework for privacy attacks in FL: https://github.com/JonasGeiping/breaching ...
【五期邹昱夫】CCF-A(NeurIPS'20)Inverting gradients-how easy is it to break privacy in federated learning? "Geiping J, Bauermeister H, Dröge H, et al. Inverting gradients-how easy is it to break privacy in federated learning?[J]. Advances in Neural Information Processing Systems, 2020, ...